Spatial distribution characteristics and influencing factors of bladder cancer in China

Spatial distribution characteristics and influencing factors of bladder cancer in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial distribution characteristics and influencing factors of bladder cancer in China Feidi Li, Jiaxin Liu, Guangyuan Liu, Qi Chen, Shuren Sun, Mengqi Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8143352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract [Background] Under the dual pressures of environmental pollution and climate change, the high incidence of bladder cancer has become a major public health problem in many countries and regions, and the disease burden is increasing. [Objective] To analyze the spatial distribution patterns of the incidence of bladder cancer in China, and to identify the influences of socioeconomic, ecological, and meteorological factors. [Methods] The incidence of bladder cancer in 178 county-level administrative units was obtained from 'China Cancer Registry Annual Report 2017–2019', and data on air quality, meteorological conditions and economic development during the same period were collected through online public channels. Spatial scan statistics were used to analyze spatial patterns of bladder cancer incidence. Spatial regression model was use to identify the associations between the influence factors and bladder cancer incidence. [Results] The incidence of bladder cancer in men was 4.41 times to women. For men, there are regional differences in the incidence of bladder cancer, and the eastern region is significantly higher than the central region. For women, the incidence of bladder cancer in the eastern region is higher than that in the central and western regions. For men, number of beds in medical institutions per 1000 people was positively correlated with the incidence of bladder cancer, while air quality index (AQI) and annual average temperature were negatively correlated. For women, number of beds in medical institutions per 1000 people, the average annual sunshine hours, the average altitude and the topographic relief of the land surface were positively correlated with the incidence of bladder cancer, while average annual temperature and annual rainfall were negatively correlated. [Conclusions] The incidence of bladder cancer was higher in men than in women. Significant spatial heterogeneity exists in the distribution of bladder cancer in China, with high-risk clusters found in the eastern region. Air quality, climatic conditions, and medical level have an impact on the incidence of bladder cancer in men and women. Environmental pollution Climate change Spatial scan Spatial regression Association Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Bladder cancer poses a significant threat to human life and contributes significantly to cancer-related mortality(Babjuk 2017; Cumberbatch et al, 2018; He et al, 2020a; Lenis et al, 2020b). Approximately 550,000 cases of bladder cancer are diagnosed worldwide annually (Ferlay et al, 2010; Figueroa et al, 2016; Richters et al, 2020). Since 2012, a significant number of newly diagnosed bladder cancer cases, totaling over 118,000, have been reported in 40 European countries, and claimed approximately 52,000 lives during that period(Marcos-Gragera et al, 2015). It is estimated that there were 500,000 new cases of bladder cancer worldwide, resulting in 200,000 deaths in 2020(Lenis et al, 2020a; Richters et al, 2020b). In the United States alone, over 80,000 new cases and 17,000 deaths are attributed to bladder cancer annually(Lenis et al. 2020a; Richters et al. 2020b; Siegel et al. 2023). The uneven geographical distribution of bladder cancer incidence and mortality may be attributed to various influential factors, including geographic and other determinants(Amin et al, 2019). In China, bladder cancer is a prevalent malignancy characterized by high incidence rates and expensive treatment costs(Xu et al. 2021; Wu et al. 2023). Recent updates from the International Agency for Research on Cancer (IARC) revealed a general decrease in incidence and mortality rates across most Western countries, whereas some Eastern European countries and developing nations have experienced an increase in these rates(Chavan et al, 2014). Given the elevated prevalence of bladder cancer and its propensity for recurrence following treatment(Stenzl et al, 2011; Lenis et al, 2020), the burden on health services is enormous and needs to be given high priority(Leal et al, 2016; Wong et al, 2018; Xia et al, 2022). Various factors influence the incidence of bladder cancer, with genetic susceptibility playing a significant role(Burger et al, 2013; Rothenberg 2015; Figueroa et al, 2016; Byrne et al, 2023). First-degree relatives of individuals with bladder cancer have a significantly higher risk of developing the disease, which is estimated to be approximately twice as high as that of the general population(Burger et al, 2013). Factors such as slow acetylation may not intrinsically lead to bladder cancer but may confer additional risk to exposure to carcinogens such as tobacco products(Burger et al, 2013). Smoking is the primary and widely recognized risk factor for bladder cancer(Burger et al, 2013; Sanli et al, 2017; Xiong et al, 2022; Seisen et al, 2023). The proportion of smokers among all patients with bladder cancer is about half(Burger et al, 2013). Evidence indicates that alterations in smoking prevalence account for only a portion of the observed changes in bladder cancer incidence, highlighting the multifaceted nature of its etiology(Seisen et al, 2023). Air pollution is related to the incidence and mortality of bladder cancer to some extent (Sanli et al, 2017; Chen et al, 2022). In Taiwan, a matched case-control study showed that the incidence of bladder cancer increases with increasing levels of air pollution(Liu et al, 2009). Sex is a notable determinant of the occurrence of bladder cancer, with men being disproportionately affected compared to women. Men have a notable three to four-fold higher incidence of bladder cancer than women(Dobruch et al, 2016). The incidence of bladder cancer is spatially and temporally heterogeneous, and changes in lifestyle and ecology may be associated with the development of bladder cancer(Smith et al, 2016). In many countries and regions, environmental pollution and climate change have increased the burden of disease, including bladder cancer. The comprehensive utilization of spatial analysis technology and traditional epidemiological research methods can more effectively analyze the spatial and temporal distribution pattern of bladder cancer and its related influencing factors (Saint-Jacques et al, 2016). These variations can be divided into two main categories: anthropogenic and natural variations. Anthropogenic factors encompass a range of economic and social aspects, including GDP per capita and availability of medical resources. Additionally, they involve environmental quality factors such as air pollution and land-use practices. On the other hand, natural factors comprise geographic elements such as mean elevation and relief degree of the land surface, as well as climatic factors like rainfall and temperature(Zhang et al, 2023a; Zhang et al, 2023b). The main objective of this study was to investigate potential factors affecting the incidence of bladder cancer in mainland China. First, we characterized the spatial aggregation of bladder cancer using spatial scanning technique. And then, spatial regression model was used to identify the association between bladder cancer and environmental factors. Finally, countermeasures and suggestions for the prevention and treatment of bladder cancer were proposed. 2 Materials and methods 2.1 Data sources and Pre-Processing The incidence data of bladder cancer were derived from 'China Cancer Registry Annual Report 2017–2019'. In the study year, 178 counties (districts) provided data for three consecutive years. In order to eliminate the influence of age structure in different regions on the results, we used the world standardized incidence rate. Air quality data, such as AQI, were acquired from the Atmospheric Composition Analysis Group at Dalhousie University and sourced from NASA satellite measurements. Meteorological data, such as mean annual temperature, mean annual precipitation, and mean annual sunshine hours, were obtained from the National Weather Science Data Center. The socioeconomic data utilized in this study, such as GDP, total grain yield, the ratio of beds in healthcare institutions to every 1000 individuals, and urbanization rate, were collected from the 2014–2016 to China County Statistical Yearbook. In addition, the mean elevation and degree of relief on the land surface were obtained from the Advanced Satellite-Borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model ( ASTER GDEM) (30 m spatial resolution). 2.2 Spatial scan Spatial scan statistics were used to analyze spatial patterns of bladder cancer incidence. By calculating the log-likelihood ratio ( LLR ) values for all window sizes and locations, the largest log-likelihood ratio value was identified. This value indicates the area where disease aggregation is most likely to occur and least likely to be caused by random variation(Takahashi et al, 2008). 2.3 Spatial regression Spatial regression analysis can comprehensively address the spatial autocorrelation and heterogeneity present in spatial data. Spatial regression model was use to quantify the associations between the influence factors and bladder cancer incidence(Zhang et al, 2023a; Zhang et al, 2023b). This approach allows for a more effective utilization and analysis of the spatial attributes inherent to such data. 3 Results and analysis 3.1 Basic distribution characteristics and gender differences We selected 178 counties (districts) that provided consistent data in the ‘China Tumor Registry Annual Report 2017–2019’. The three-year average land area of these selected districts was 450,868 square kilometers and the average total population was 115,722,356. From 2014 to 2016, 16,001 cases of bladder cancer were reported, of which 12,528 (78.3%) were male and 3,473 (21.7%) were female. The average crude incidence rate of bladder cancer in the 178 tumor registry areas was calculated to be 4.6 cases per 100,000 people. Specifically, regarding the male population, there was a mean incidence rate of 3.6 cases of bladder cancer per 100,000 individuals, whereas for the female population, the mean incidence rate was noted as 1.0/100,000 individuals. Furthermore, when considering the standardized incidence rate, the average rate for males was 4.91, while for females it was 1.31. This analysis was conducted using the SPSS 25 software. The male-to-female sex ratio was calculated to be 4.41 (95% CI : 4.04–4.79, p < 0.001). The spatial distribution of average standardized incidence rate (ASIRW) for men and women with bladder cancer in China from 2014 to 2016 are shown in Fig. 1 and Fig. 2 . 3.2 Spatial distribution characteristics By means of spatial scan on the incidence of bladder cancer in men, we identified eight statistically significant clusters, encompassing a total of 112 counties (Table 1 and Fig. 3 ). In the group of regions with high-incidence clusters, the primary cluster ( LLR = 270.51, RR = 1.81, P < 0.001) comprised Jiangsu and Zhejiang province, consisting of a total of 14 counties (districts). The second cluster ( LLR = 213.31, RR = 2.22, P < 0.001) encompassed seven counties (districts) in the Liaoning and Shandong province. Within the third cluster ( LLR = 52.36, RR = 1.53, P < 0.001), there were 14 counties (districts) located in Sichuan, Yunnan, and Guizhou Province. The fourth cluster ( LLR = 23.01, RR = 1.23, P < 0.001) consisted of nine counties (districts) in Shandong and Jiangsu province. For low incidence clusters, the primary cluster ( LLR = 351.00, RR = 0.49, P < 0.001) included 30 counties (districts) spread across Shanxi, Hebei, Shaanxi, Henan, and Shandong province. In the second cluster ( LLR = 80.09, RR = 0.7, P < 0.001), 29 counties (districts) were located in Jiangxi, Hunan, Hainan, Fujian, Guangxi, and Guangdong. The third cluster ( LLR = 41.88, RR = 0.33, P < 0.001) was observed in two counties (districts) in Sichuan. The fourth cluster ( LLR = 23.44063, RR = 0.77, P < 0.001) encompassed seven counties (districts) within Jiangsu Province. Table 1 Results of spatial scanning analysis of incidence of bladder cancer in men in China Statistic High-mortality cluster Low-mortality cluster 1 2 3 4 1 2 3 4 Number of cities 14 7 14 9 30 29 2 7 Radius (km) 194.04 339.48 895.34 168.17 362.92 738.88 120.72 72.24 Observed cases 2094 897 687 1223 1302 1315 46 643 Expected cases 1247.67 419.88 456.82 1010.02 2385.43 1790.84 138.1 826.24 RR 1.81 2.22 1.53 1.23 0.49 0.7 0.33 0.77 LLR 270.51 213.31 52.36 23.01 351 80.09 41.88 23.44 P value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Upon analyzing the incidence of bladder cancer in women, four statistically significant clusters encompassing 94 counties were identified (Table 2 and Fig. 4 ). Two high-incidence clusters were identified in the analysis. The first cluster ( LLR = 106.33, RR = 2.39, P < 0.001) consisted of six counties (districts) located in southern Jiangsu and Zhejiang province. The second cluster ( LLR = 72.12, RR = 2.39, P < 0.001) spanned 23 counties (districts) in Liaoning, Shandong, Hebei, Jilin, Inner Mongolia, and Heilongjiang province. The primary cluster ( LLR = 31.59, RR = 0.59, P < 0.001) comprised 38 counties (districts) situated in Hunan, Hubei, Jiangxi, Henan, Shaanxi, and Sichuan province. The second cluster ( LLR = 16.25, RR = 0.75, P < 0.001) covered 27 counties (districts) located in Shanxi, Hebei, Henan, and Shandong province. Table 2 Results of spatial scanning analysis of incidence of bladder cancer in women in China Statistic High-mortality cluster Low-mortality cluster 1 2 1 2 Number of cities 6 23 38 27 Radius (km) 138.80 1102.54 501.50 378.66 Observed cases 396 578 533 427 Expected cases 177.77 347.75 816.9 545.36 RR 2.39 1.79 0.59 0.75 LLR 106.33 72.12 70.97 16.25 P value < 0.001 < 0.001 < 0.001 < 0.001 3.3 Results of spatial regression By means of spatial regression, the associations between the incidence of bladder cancer and various factors including air quality, climatic conditions, socioeconomic indicators, and geographic factors were identified. The independent variables considered in the analysis included the air quality index (AQI), mean annual temperature, mean annual rainfall, mean annual hours of sunlight, total food production, gross domestic product (GDP), urbanization rate, the number of beds in medical institutions per 1000 people, mean elevation, and the degree of relief on the land surface. The dependent variables were the incidence of bladder cancer in men and women. Results showed that there was a positive association between number of beds in medical institutions per 1000 people ( β = 0.334, P < 0.001) and the incidence of bladder cancer in men. Conversely, a negative correlation was observed between the air quality index (AQI) ( β = -0.251, P < 0.05) and mean annual temperature ( β = -0.416, P < 0.05) and the incidence of bladder cancer in men (Table 3 ). Table 3 Results of model screening and fitting of bladder cancer incidence and influencing factors in men Variable TYPE Coefficient Std. Error z-value Probability AQI SEM -0.251 0.12 -2.089 0.03667 Total food production SLM -0.191 0.1 -1.916 0.05537 GDP SLM 0.287 0.265 1.082 0.27939 The ratio of beds in health-care institutions to every 1000 individuals SLM 0.334 0.098 3.419 0.00063 Urbanization rate SLM 0.184 0.099 1.869 0.06166 Mean annual temperature SEM -0.416 0.163 -2.552 0.01072 Mean annual rainfall SLM -0.092 0.101 -0.906 0.36473 Mean annual hours of sunlight SLM 0.034 0.023 1.473 0.14067 Mean elevation SLM 0.169 0.099 1.704 0.0884 Degree of relief in the land surface SLM 0.125 0.099 1.257 0.20858 Meanwhile, a positive correlation was observed between the number of beds in medical institutions per 1000 people ( β = 0.111, P < 0.05), mean annual hours of sunlight ( β = 0.044, P < 0.05), and mean elevation and the degree of relief in the land surface ( β = 0.158, P < 0.05) with the incidence of bladder cancer in women. Conversely, negative correlations were found between the mean annual temperature ( β = -0.226, P < 0.001) and mean annual rainfall ( β = -0.102, P < 0.05) with the incidence of bladder cancer in women (Table 4 ). Table 4 Results of model screening and fitting of bladder cancer incidence and influencing factors in women Variable TYPE Coefficient Std. Error z-value Probability AQI SEM -0.083 0.049 -1.68 0.09302 Total food production SLM -0.032 0.041 -0.797 0.42573 GDP SLM -0.07 0.109 -0.646 0.51854 The ratio of beds in health-care institutions to every 1000 individuals SLM 0.111 0.04 2.743 0.00609 Urbanization rate SEM -0.021 0.044 -0.478 0.63259 Mean annual temperature SEM -0.226 0.063 -3.583 0.00034 Mean annual rainfall SLM -0.102 0.042 -2.408 0.01603 Mean annual hours of sunlight SEM 0.044 0.015 3.011 0.0026 Mean elevation SEM 0.158 0.05 3.126 0.00177 Degree of relief in the land surface SEM 0.158 0.055 2.903 0.00369 4 Discussion 4.1 Incidence of bladder cancer and gender difference The findings of the present study highlight a notable disparity between men and women in terms of bladder cancer development, with the most prominent risk factor being smoking, potentially explaining this difference(Antoni et al, 2017; Wong et al, 2018; Densmore et al, 2019). It is commonly reported that smoking rates tend to be higher among men compared to women(Chavan et al, 2014; Sanli et al, 2017; Densmore et al, 2019). Based on data provided by the American Cancer Society, individuals who engage in smoking have a substantially increased risk (no less than three times greater) of developing bladder cancer compared with those who do not smoke(Seisen et al, 2023). In a recent meta-analysis of multiple studies, smokers were observed to have a higher risk of developing bladder cancer than former smokers(Van Osch et al, 2019; Seisen et al, 2023). Smoking prevalence alone cannot fully account for the variation in bladder cancer incidence between men and women, indicating that other factors are also at play. It is well recognized that smoking is an important risk factor for bladder cancer, but there may be other determinants that contribute to the observed differences in incidence rates(Krabbe et al, 2015). Hence, while the sex disparity in smoking prevalence can partially account for the variation in bladder cancer incidence between men and women, it is not the sole determining factor (Hemelt et al, 2009; Dobruch et al, 2016). Therefore, in addition to smoking prevalence, bladder cancer incidence is influenced by numerous underlying biological and epidemiological factors(Dobruch et al, 2016). Although men have a higher incidence of bladder cancer, it is important to highlight that women tend to exhibit a higher propensity for advanced-stage disease diagnosis and poorer prognosis(Viswambaram and Hayne 2020). Several factors could potentially explain this disparity, including variations in carcinogenic catabolism, hormonal signaling, and exposure to risk factors. Additionally, delayed diagnosis in women, anatomical differences, and the impact of female cystectomy may also contribute to these differences(Viswambaram and Hayne 2020). Bladder cancer is a prevalent form of cancer in Taiwan, affecting both men and women(Hsiao et al, 2021). According to data from 2012, the age-standardized incidence rates for bladder cancer were reported as 8.70 and 3.34 per 100,000 population for males and females, respectively. These figures highlight the higher incidence rate among males than among females at that time. Looking ahead to 2025, projections indicate an increase in the age-standardized incidence rate for males. It is anticipated to reach 10.4 cases per 100,000 population, reflecting an upward trend. In contrast, for women, the projected age-standardized incidence rate for 2025 is expected to be 3.7 per 100,000 individuals, representing a relatively stable incidence rate within this timeframe(Hsiao et al, 2021). To provide a comprehensive analysis of the incidence of bladder cancer across different regions, we used data extracted from the GLOBOCAN database. This study focused on 32 distinct regions and presented the standardized incidence rates for bladder cancer in 2012, as outlined in Table 5 (Wong et al, 2018). The results of this study showed that there were significant differences in the incidence of bladder cancer among men in different areas. Southern Europe, Western Europe, and North America exhibited the highest incidence rates, surpassing 19 cases per 100,000 people. Conversely, West and Central Africa had the lowest recorded incidence rates at 2.1 and 2.2 cases, respectively. For females, North America and Western Europe had the highest incidence of bladder cancer, reported as 5.1 and 4.3 cases per 100,000 people, respectively. Conversely, Melanesia and South-Central Asia demonstrated the lowest recorded rates, of 0.7 and 0.8 cases, respectively(Wong et al, 2018). Additionally, this study examined the male-to-female prevalence ratio, known as the incidence rate ratio ( IRR ), for bladder cancer. Micronesia displayed the highest IRR (7.22), indicating a higher prevalence of bladder cancer in males. In contrast, West Africa showed the lowest IRR (1.62), suggesting a relatively lower sex disparity in bladder cancer prevalence within that region. These findings underscore the importance of understanding the regional variations in the incidence of bladder cancer and sex-specific disparities. Such insights are vital for identifying high-risk regions and for designing targeted prevention and early detection strategies. This study investigated the incidence of bladder cancer in 178 counties (districts) in inland China between 2014–2016. Despite the different time spans from Taiwan and other mentioned regions, the study highlighted that both male and female bladder cancer incidence rates were higher in Taiwan than in 178 counties (districts) in China. Conversely, regions such as West and Central Africa exhibited lower incidence in men and women than in China. The variation in bladder cancer incidence rates across these regions might be associated with the Human Development Index (HDI) of each country(Cumberbatch et al, 2018a; Wong et al, 2018b). Factors encompassed within the HDI, including the quality of healthcare, socioeconomic status, and access to resources, could contribute to differences in disease incidence and outcome. This study showed that the male-to-female incidence ratio in 178 counties (districts) in inland China from to 2014–2016 was 4.41. This suggests that the prevalence of bladder cancer in males is more than four times higher than that in females, with an even greater disparity when compared to that in West and East Africa. Table 5 Standardized incidence rates of bladder cancer for men and women and ratio of incidence rates for men and women, by region, 2012 World regions Male ASIWW Female ASIWW IRR Africa 6.3 2.1 3.00 Eastern Africa 3.3 2.0 1.65 Middle Africa 2.2 1.3 1.69 Northern Africa 15.1 3.2 4.72 Southern Africa 7.5 1.9 3.95 Western Africa 2.1 1.3 1.62 Asia 5.5 1.4 3.93 Eastern Asia 5.8 1.6 3.63 South-Eastern Asia 4.3 1.0 4.30 South-Central Asia 3.6 0.8 4.50 Western Asia 19.0 3.1 6.13 America 6.1 2.0 3.05 Caribbean 7.6 1.8 4.22 Central America 3.4 1.8 1.89 South America 6.9 2.1 3.29 North America 19.5 5.1 3.82 Europe 17.7 3.5 5.06 Central and Eastern Europe 15.1 2.7 5.59 Northern Europe 12.4 3.6 3.44 Southern Europe 21.8 3.8 5.74 Western Europe 19.7 4.3 4.58 Oceania 10.6 2.7 3.93 Australia/New Zealand 11.3 2.9 3.90 Melanesia 3.5 0.7 5.00 Micronesia/Polynesia 6.5 0.9 7.22 More developed regions 16.9 3.7 4.57 Less developed regions 5.3 1.5 3.53 World 9.0 2.2 4.09 4.2 Spatial distribution characteristics of bladder cancer The results of spatial scanning showed that the high incidence clusters of bladder cancer in men and women were mainly clustered in the eastern part of China. One of the reasons may be due to the socioeconomic differences in different regions. This suggests a potential relationship between bladder cancer incidence and socioeconomic status in different regions(Densmore et al, 2019). A study conducted in Canada observed that the incidence of bladder cancer is increasing among socioeconomically disadvantaged populations(Densmore et al, 2019). Similarly, unequal levels of economic development across various regions in China may also play a role in the spatial variability observed in bladder cancer incidence. For the incidence of bladder cancer in men, there was a significant gap between the northern and southern regions of Jiangsu Province(Fig. 3 ). This discrepancy can likely be attributed to the stronger industrial base, higher urbanization, and greater economic development in southern Jiangsu as well as the presence of more severe air pollution in the region(Cheng and Nathanail 2019). The observed high incidence clustering in certain counties (districts) within Liaoning, Shandong, and Sichuan provinces may be linked to factors such as a larger elderly population, higher level of aging, and accelerated aging process. Notably, Liaoning province ranks first in the country in terms of population aging according to the National Bureau of Statistics, with Sichuan also appearing on the list. It has been suggested that overall growth and aging of the population are major contributing factors to the increased incidence of bladder cancer worldwide(Richters et al, 2020; Zi et al, 2021). Regarding the clustering of high incidence in female bladder cancer, it was observed that the eastern region surpasses the central and western regions, with a concentrated high incidence in economically advanced regions such as northern Zhejiang, southern Jiangsu, and northeastern China. The reasons for the high incidence clustering in northern and southern Zhejiang align with those for male bladder cancer. The high incidence clustering in northeastern China may be attributed to the shifting weight of the elderly population rate (EPR) and elderly dependency ratio (EDR) towards the northeastern region. This shift could potentially increase the economic burden of elderly care in Northeast China. Consequently, future development policies should prioritize socioeconomic growth and the allocation of elderly care resources, particularly in northern China(Man et al, 2021). Future development policies should thus concentrate on fostering socioeconomic growth and ensuring the allocation of resources for elderly care, with a particular emphasis on northern China. Multiple factors account for the disparities in the spatial scan maps of the incidence of bladder cancer incidence in men and women. Smoking, delayed diagnosis in women, anatomical variance, and female cystectomy, as mentioned earlier, all contribute to some extent to the differences in incidence rates between the sexes. Additionally, variations in sex ratios and lifestyle choices across different regions in China may also contribute to spatial disparities in bladder cancer incidence rates between men and women(Byrne et al, 2023). Consequently, these factors are responsible for the spatial disparities observed in the incidence of bladder cancer between males and females. Bladder cancer primarily affects the elderly population aged ≥ 60 years and exhibits a higher incidence rate. As individuals grow older, the deterioration of vital organ function, weakened immunity, and overall physical fitness are significant contributing factors to the development of bladder cancer. Consequently, these factors increase the complexity of clinical care and impose a greater burden on society(Richters et al, 2020). Hence, it is crucial to enhance the scope of early bladder cancer screening and advocate standardized and consistent clinical care for bladder cancer. These measures are essential to reduce the mortality rate of bladder cancer in China and alleviate the burden on the healthcare system. Furthermore, it is important to objectively and comprehensively examine the relationship between specific factors and bladder cancer incidence. 4.3 Potential environmental influences on bladder cancer In this study, we observed a negative association between air quality index and the incidence of bladder cancer in men. Nevertheless, it is worth mentioning that air pollution contributes to an increased risk of bladder cancer to some extent(Castano-Vinyals et al, 2008; Zhang et al, 2022; Haghayegh et al, 2023). Further investigations are needed to determine whether the air quality index (AQI) affects bladder cancer susceptibility. Regarding the relationship between elevation and cancer, some ecological studies have found that people living at high altitudes and in places with the highest UV radiation are less likely to develop different types of cancers (Calderon-Gerstein and Torres-Samaniego 2021). However, some scientists have also reported an increased incidence of different types of cancer in the same areas(Calderon-Gerstein and Torres-Samaniego 2021). A study conducted by researchers in 2011 on high altitude and cancer mortality showed no significant difference between altitude and cancer mortality(Ezzati et al, 2012; Calderon-Gerstein and Torres-Samaniego 2021). In the present study, the mean elevation and relief of the land surface were associated with the incidence of bladder cancer in women. The reason may be the increase of UV radiation, reduce of availability of fruits and vegetables, decrease of temperature, low selenium levels in the soil, or contamination with mineral elements such as arsenic, among others, which increases the risk of cancer(Calderon-Gerstein and Torres-Samaniego 2021; Pal et al, 2022). Regarding the relationship between bladder cancer incidence and temperature and precipitation, there are few studies in this area at home and abroad. Several studies have provided evidence suggesting an inverse correlation between Polycyclic Aromatic Hydrocarbons (PAHs) and temperature, which suggests that as temperatures rise, the concentration of PAHs decreases, thereby potentially lowering the associated cancer risk(Khan et al, 2018). This is in line with the results of this study that bladder cancer and temperature were negatively correlated, and the relationship between bladder cancer incidence and precipitation needs to be further explored. In this study, the investigation of the relationship between sunshine hours and cancer revealed interesting findings. In the case of women, there was a negative association, indicating that increased sunshine hours were linked to a lower incidence of bladder cancer. These results raise intriguing questions concerning the potential sex-specific effects of sunshine hours on bladder cancer risk. The underlying mechanisms behind this sex disparity remain unclear and warrant further investigation. Biological factors, such as hormonal differences, may contribute to the varying responses to sunlight exposure between men and women. Additionally, lifestyle behaviors and cultural practices related to sun exposure could also influence the observed associations. It is important to note that while this study suggests a potential link between sunshine hours and bladder cancer incidence in women, it does not establish a causal relationship. Other factors, such as genetic predisposition, dietary patterns, and occupational exposure, need to be considered when comprehensively assessing bladder cancer risk. Recent studies have shed light on the correlation between the incidence of bladder cancer and external factors in men and women. Notably, findings have consistently highlighted a positive relationship between GDP per capita and the occurrence of bladder cancer in both genders(Teoh et al, 2020). This suggests that regions with higher economic status are more prone to an increased risk of bladder cancer. The underlying causes of this association could be multifaceted, encompassing lifestyle choices, exposure to environmental pollutants, or disparities in healthcare access and awareness. In parallel, the number of beds in medical institutions per 1000 people has been recognized as a representative measure of local healthcare resources. Areas equipped with greater healthcare resources often exhibit enhanced medical care provision and more precise screening protocols, contributing to a heightened detection rate of bladder cancer cases. This emphasizes the pivotal role of accessible and well-equipped healthcare facilities in the early diagnosis and timely intervention of patients with bladder cancer. Interestingly, total food production and urbanization rate were not significantly associated with bladder cancer incidence in either sex. These findings indicate that variables related to agricultural productivity and the level of urban development do not directly affect the likelihood of bladder cancer development among men and women. Moreover, the study outcomes underscore the existence of disparities not only in the incidence rates of bladder cancer between men and women but also in the susceptibility of each sex to external influencing factors. Biological, hormonal, and behavioral differences may contribute to these variations, necessitating further research to unravel the underlying mechanisms and tailor prevention strategies accordingly. 5 Conclusion In this paper, the spatial distribution characteristics of bladder cancer incidence in mainland China were revealed for the first time with county-level administrative units as the spatial resolution, and the effects of economic society, natural conditions and air quality were analyzed. The incidence of bladder cancer was higher in men than in women. The spatial distribution of bladder cancer in mainland China revealed substantial heterogeneity, characterized by concentrated high-risk clusters in the eastern region. Air quality, climatic conditions, and medical level have an impact on the incidence of bladder cancer in men and women. It should be pointed out that this paper is a retrospective study, which is limited to relevance, not causality. Future studies should focus on unraveling the underlying mechanisms driving these spatial epidemiological trends and incorporating the analysis of potential risk factors into comprehensive analyses. Declarations Ethics approval No statement of ethical approval as there are no ethical implications in this paper. Consent to participate and publish All information was de-identified, and we do not need patients’ informed consent. Consent to publication Not applicable. Availability of data and materials The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. Founding This work was supported by Fujian Provincial Joint Science and Technology Innovation Funding Project (2021Y9117) and Horizontal Scientific Research Project of Wenzhou Medical University (KJHX2014). Authorship contribution statement Feidi Li: Methodology, Data curation, Software, Formal analysis, Visualization, Writing-original draft, Writing-review & editing. Jiaxin Liu, Guangyuan Liu, Qi Chen, Shuren Sun: Methodology, Data curation, Writing-review & editing. Mengqi Zhang, Shuhui Huang, Peipei Zhou and Zhonglu Liao: Methodology, Formal analysis, Writing-review & editing. Hong Huang and Zhigang Wu: Idea, Methodology, Resource, Writing-review & editing. References Amin, R. W., B. Stafford, et al (2019). "A spatial study of bladder cancer mortality and incidence in the contiguous US: 2000–2014." Sci Total Environ 670 : 806–813. Antoni, S., J. Ferlay, I. Soerjomataram, et al (2017). "Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends." Eur Urol 71 (1): 96–108. Babjuk, M. (2017). "Trends in Bladder Cancer Incidence and Mortality: Success or Disappointment?" Eur Urol 71 (1): 109–110. Burger, M., J. W. Catto, G. Dalbagni, et al (2013). "Epidemiology and risk factors of urothelial bladder cancer." Eur Urol 63 (2): 234–241. Byrne, S., T. Boyle, M. Ahmed, et al (2023). "Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types." Int J Epidemiol 52 (3): 817–826. Calderon-Gerstein, W. S., G. Torres-Samaniego (2021). "High altitude and cancer: An old controversy." Respir Physiol Neurobiol 289 : 103655. Castano-Vinyals, G., K. P. Cantor, N. Malats, et al (2008). "Air pollution and risk of urinary bladder cancer in a case-control study in Spain." Occup Environ Med 65 (1): 56–60. Chavan, S., F. Bray, J. Lortet-Tieulent, et al (2014). "International variations in bladder cancer incidence and mortality." Eur Urol 66 (1): 59–73. Chen, J., S. Rodopoulou, M. Strak, et al (2022). "Long-term exposure to ambient air pollution and bladder cancer incidence in a pooled European cohort: the ELAPSE project." Br J Cancer 126 (10): 1499–1507. Cheng, Y. and C. P. Nathanail (2019). "A study of "cancer villages" in Jiangsu Province of China." Environ Sci Pollut Res Int 26 (2): 1932–1946. Cumberbatch, M. G. K., I. Jubber, P. C. Black, et al (2018). "Epidemiology of Bladder Cancer: A Systematic Review and Contemporary Update of Risk Factors in 2018." Eur Urol 74 (6): 784–795. Densmore, R., M. Hajizadeh, M. Hu (2019). "Trends in socio-economic inequalities in bladder cancer incidence in Canada: 1992–2010." Can J Public Health 110 (6): 722–731. Dobruch, J., S. Daneshmand, M. Fisch, et al (2016). "Gender and Bladder Cancer: A Collaborative Review of Etiology, Biology, and Outcomes." Eur Urol 69 (2): 300–310. Ezzati, M., M. E. Horwitz, D. S. Thomas, et al (2012). "Altitude, life expectancy and mortality from ischaemic heart disease, stroke, COPD and cancers: national population-based analysis of US counties." J Epidemiol Community Health 66 (7): e17. Ferlay, J., H. R. Shin, F. Bray, et al (2010). "Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008." Int J Cancer 127 (12): 2893–2917. Figueroa, J. D., C. D. Middlebrooks, A. R. Banday, et al (2016). "Identification of a novel susceptibility locus at 13q34 and refinement of the 20p12.2 region as a multi-signal locus associated with bladder cancer risk in individuals of European ancestry." Hum Mol Genet 25 (6): 1203–1214. Haghayegh, S., Y. Liu, Y. Zhang, et al (2023). "Rotating Night Shift Work and Bladder Cancer Risk in Women: Results of Two Prospective Cohort Studies." Int J Environ Res Public Health 20 (3). He, H., H. Xie, Y. Chen, et al (2020). "Global, regional, and national burdens of bladder cancer in 2017: estimates from the 2017 global burden of disease study." BMC Public Health 20 (1): 1693. Hemelt, M., H. Yamamoto, K. K. Cheng, et al (2009). "The effect of smoking on the male excess of bladder cancer: a meta-analysis and geographical analyses." Int J Cancer 124 (2): 412–419. Hsiao, B. Y., S. Y. Su, J. R. Jhuang, et al (2021). "Ensemble forecasting of a continuously decreasing trend in bladder cancer incidence in Taiwan." Sci Rep 11 (1): 8373. Khan, M. B., M. Masiol, C. Bruno, et al (2018). "Potential sources and meteorological factors affecting PM(2.5)-bound polycyclic aromatic hydrocarbon levels in six main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks." Environ Sci Pollut Res Int 25 (32): 31987–32000. Krabbe, L. M., R. S. Svatek, S. F. Shariat, et al (2015). "Bladder cancer risk: Use of the PLCO and NLST to identify a suitable screening cohort." Urol Oncol 33 (2): 65 e19-25. Leal, J., R. Luengo-Fernandez, R. Sullivan, et al (2016). "Economic Burden of Bladder Cancer Across the European Union." Eur Urol 69 (3): 438–447. Lenis, A. T., P. M. Lec, K. Chamie, et al (2020). "Bladder Cancer: A Review." JAMA 324 (19): 1980–1991. Liu, C. C., S. S. Tsai, H. F. Chiu, et al (2009). "Ambient exposure to criteria air pollutants and risk of death from bladder cancer in Taiwan." Inhal Toxicol 21 (1): 48–54. Man, W., S. Wang, H. Yang (2021). "Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China." BMC Public Health 21 (1). Marcos-Gragera, R., S. Mallone, L. A. Kiemeney, et al (2015). "Urinary tract cancer survival in Europe 1999–2007: Results of the population-based study EUROCARE-5." Eur J Cancer 51 (15): 2217–2230. Pal, L., T. Jenei, M. McKee, et al (2022). "Health and economic gain attributable to the introduction of the World Health Organization's drinking water standard on arsenic level in Hungary: A nationwide retrospective study on cancer occurrence and ischemic heart disease mortality." Sci Total Environ 851 (Pt 2): 158305. Richters, A., K. K. H. Aben, L. Kiemeney (2020). "The global burden of urinary bladder cancer: an update." World J Urol 38 (8): 1895–1904. Rothenberg, R. (2015). "The causes of cancer, revisited." Ann Epidemiol 25 (3): 215–216. Saint-Jacques, N., J. S. W. Lee, P. Brown, et al (2016). "Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada." BMC Public Health 16 : 175. Sanli, O., J. Dobruch, M. A. Knowles, et al (2017). "Bladder cancer." Nat Rev Dis Primers 3 : 17022. Seisen, T., M. Labban, S. R. Lipsitz, et al (2023). "Assessment of the Ecological Association between Tobacco Smoking Exposure and Bladder Cancer Incidence over the Past Half-Century in the United States." Curr Oncol 30 (2): 1986–1998. Siegel, R. L., K. D. Miller, N. S. Wagle, et al (2023). "Cancer statistics, 2023." CA Cancer J Clin 73 (1): 17–48. Smith, N. D., S. M. Prasad, A. R. Patel, et al (2016). "Bladder Cancer Mortality in the United States: A Geographic and Temporal Analysis of Socioeconomic and Environmental Factors." J Urol 195 (2): 290–296. Stenzl, A., N. C. Cowan, M. De Santis, et al (2011). "Treatment of muscle-invasive and metastatic bladder cancer: update of the EAU guidelines." Eur Urol 59 (6): 1009–1018. Takahashi, K., M. Kulldorff, T. Tango, et al (2008). "A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring." Int J Health Geogr 7 : 14. Teoh, J. Y., J. Huang, W. Y. Ko, et al (2020). "Global Trends of Bladder Cancer Incidence and Mortality, and Their Associations with Tobacco Use and Gross Domestic Product Per Capita." Eur Urol 78 (6): 893–906. van Osch, F. H. M., J. Vlaanderen, S. H. J. Jochems, et al (2019). "Modeling the Complex Exposure History of Smoking in Predicting Bladder Cancer: A Pooled Analysis of 15 Case-Control Studies." Epidemiology 30 (3): 458–465. Viswambaram, P., D. Hayne (2020). "Gender discrepancies in bladder cancer: potential explanations." Expert Review of Anticancer Therapy 20 (10): 841–849. Wong, M. C. S., F. D. H. Fung, C. Leung, et al (2018). "The global epidemiology of bladder cancer: a joinpoint regression analysis of its incidence and mortality trends and projection." Sci Rep 8 (1): 1129. Wu, R., X. Teng, Q. Song, et al (2023). "Single-cell RNA sequencing reveals sexual diversity in the human bladder and its prospective impacts on bladder cancer and urinary tract infection." BMC Med Genomics 16 (1): 122. Xia, C., X. Dong, H. Li, M. Cao, et al (2022). "Cancer statistics in China and United States, 2022: profiles, trends, and determinants." Chin Med J (Engl) 135 (5): 584–590. Xiong, J., L. Yang, Y. Q. Deng, et al (2022). "The causal association between smoking, alcohol consumption and risk of bladder cancer: A univariable and multivariable Mendelian randomization study." Int J Cancer 151 (12): 2136–2143. Xu, Y., C. Luo, J. Wang, et al (2021). "Application of nanotechnology in the diagnosis and treatment of bladder cancer." J Nanobiotechnology 19 (1): 393. Zhang, H. W., Z. R. Tsai, V. C. Kok, et al (2022). "Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer." Sci Rep 12 (1): 20799. Zhang, M., X. Dai, G. Chen, et al (2023). "Analysis of the distribution characteristics of prostate cancer and its environmental factors in China." Environ Sci Pollut Res Int 30 (11): 29349–29368. Zhang, M., X. Dai, G. Chen, et al (2023). "The Association between Spatial-Temporal Distribution of Prostate Cancer and Environmental Factors in Mainland China." Cancer Epidemiol Biomarkers Prev 32 (2): 208–216. Zi, H., S. H. He, X. Y. Leng, et al (2021). "Global, regional, and national burden of kidney, bladder, and prostate cancers and their attributable risk factors, 1990–2019." Mil Med Res 8 (1): 60. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8143352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552934347,"identity":"f9b28aa4-8b43-4980-8005-a249a3d4fbb0","order_by":0,"name":"Feidi Li","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feidi","middleName":"","lastName":"Li","suffix":""},{"id":552934349,"identity":"ccd2e8a5-2ef7-4aae-be43-de0461953fb1","order_by":1,"name":"Jiaxin Liu","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Liu","suffix":""},{"id":552934350,"identity":"2dd33b6a-b858-47cf-ad5c-efa349ff74f7","order_by":2,"name":"Guangyuan Liu","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangyuan","middleName":"","lastName":"Liu","suffix":""},{"id":552934351,"identity":"0a55a024-d84b-45bf-bfb2-64a3ed6d70d8","order_by":3,"name":"Qi Chen","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Chen","suffix":""},{"id":552934352,"identity":"4dc8d41d-7523-4e36-a02a-ecf15c07e50c","order_by":4,"name":"Shuren Sun","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuren","middleName":"","lastName":"Sun","suffix":""},{"id":552934355,"identity":"44cfddc8-07ed-4316-a7fd-63ef1bbebc01","order_by":5,"name":"Mengqi Zhang","email":"","orcid":"","institution":"School and Hospital of Stomatology, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Zhang","suffix":""},{"id":552934357,"identity":"89714a4a-5dff-44c5-a5c2-1aa9a72227e7","order_by":6,"name":"Shuhui Huang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuhui","middleName":"","lastName":"Huang","suffix":""},{"id":552934359,"identity":"6433357c-05de-47bb-9f58-36f208e1bdc6","order_by":7,"name":"Peipei Zhou","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Zhou","suffix":""},{"id":552934360,"identity":"f224fa05-423e-4fbe-b3be-70e986d0913b","order_by":8,"name":"Zhonglu Liao","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhonglu","middleName":"","lastName":"Liao","suffix":""},{"id":552934361,"identity":"56128ecd-7927-4116-b753-5ec96d173ef4","order_by":9,"name":"Hong Huang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Huang","suffix":""},{"id":552934362,"identity":"0308fa25-200a-47b8-8ea7-5f415ee14644","order_by":10,"name":"Zhigang Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYHACAwaGAijzA1yEoBaoGsYZJGth5iFGC//s5m0fPhjY2G24kf74tW3btsQG9uZtEgw1d3BqkbhzrHjmDIO05A03EtKsc9tuJzbwHCuTYDj2DLc1N3KMmXkMDicb3E44Zpy7DahFIsdMgrHhME4d8ggtiW3GliAt8m/wazGAarEzuJ3M/JgRbAsPfi2GN9KKGYF+SZC8/4yNsfffbeM2nrRii4RjuLXI3UjezPChwsae78zxxx9+nLkt289+eOONDzW4tcBA4oIDDGwSIBYbiEggqIGBwV6+gYH5AxEKR8EoGAWjYAQCAPc0WhqlpbtyAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-11-18 09:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8143352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8143352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97257791,"identity":"f8a00419-99f3-4916-9479-1139a9f3d3ef","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":773194,"visible":true,"origin":"","legend":"","description":"","filename":"SpatialdistributioncharacteristicsandinfluencingfactorsofbladdercancerinChina20251117.docx","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/418f614475d9913409d7f88a.docx"},{"id":97367606,"identity":"288a8505-8fc0-4f01-a2d9-2ad411d3b15b","added_by":"auto","created_at":"2025-12-03 16:19:47","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11741,"visible":true,"origin":"","legend":"","description":"","filename":"0f9643eea6ea47f9a17883403b52c842.json","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/6085e868c32b0e8f896edd01.json"},{"id":97367864,"identity":"78a3cf5b-6056-4954-836e-a70990bbdb54","added_by":"auto","created_at":"2025-12-03 16:20:55","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115346,"visible":true,"origin":"","legend":"","description":"","filename":"0f9643eea6ea47f9a17883403b52c8421enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/f3cbe6477de208f34328847f.xml"},{"id":97367898,"identity":"98f9b2ce-1c65-41f3-9593-4d8465d79b8c","added_by":"auto","created_at":"2025-12-03 16:20:59","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111844,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/55b2eecc4f97db54994ea0b6.png"},{"id":97257793,"identity":"65dbe207-4f3a-4def-8425-934ef8db1756","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117396,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/51c2f5a21625b7cc93447af9.png"},{"id":97367728,"identity":"3d7aba1d-7ce4-4f92-bbbc-0bed074eeee9","added_by":"auto","created_at":"2025-12-03 16:20:28","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75697,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/489e42fbc759b6d6c4fefb12.png"},{"id":97257786,"identity":"5d24221d-2996-4d07-862c-442953a7b715","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76471,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/526a6fe14c5a96006708c365.png"},{"id":97257789,"identity":"ff4d27ed-67a0-405b-b1ee-a6bc5773872a","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115125,"visible":true,"origin":"","legend":"","description":"","filename":"0f9643eea6ea47f9a17883403b52c8421structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/0bf50ccc40da117c63dfdfc4.xml"},{"id":97367485,"identity":"d6fa4478-6a64-4877-a457-029b952ea49e","added_by":"auto","created_at":"2025-12-03 16:18:56","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125343,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/9347edbadba46ae4937c15e3.html"},{"id":97257781,"identity":"7dcd7aae-e3b0-452a-9c30-5ed1cf171ef4","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160786,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of average standardized incidence rate(ASIRW) for bladder cancer among men in China\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/efc9ab364c03b0af981bcdee.jpeg"},{"id":97257783,"identity":"5f553c36-98f0-44ff-94bb-d9e63838dc44","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179267,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of average standardized incidence rate(ASIRW) for bladder cancer among women in China\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/d6c231ca53e0c3a5928b7014.jpeg"},{"id":97257782,"identity":"c349e7fe-be11-4378-b185-0064dea6a2f5","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121366,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial aggregation of the incidence of bladder cancer in men in China\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/34a8e1bcbbaad8c79e081712.jpeg"},{"id":97257784,"identity":"a8df1ec0-33d8-47de-a9e6-9bc7302e8769","added_by":"auto","created_at":"2025-12-02 13:42:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109421,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial aggregation of the incidence of bladder cancer in women in China\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/091cada5636c55043d1c77bb.jpeg"},{"id":99308363,"identity":"3c32a338-23b8-4777-b974-73ac4fbfd233","added_by":"auto","created_at":"2025-12-31 16:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8143352/v1/a378b034-8d7b-4448-ba2c-f19a4e3dc019.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial distribution characteristics and influencing factors of bladder cancer in China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBladder cancer poses a significant threat to human life and contributes significantly to cancer-related mortality(Babjuk 2017; Cumberbatch et al, 2018; He et al, 2020a; Lenis et al, 2020b). Approximately 550,000 cases of bladder cancer are diagnosed worldwide annually (Ferlay et al, 2010; Figueroa et al, 2016; Richters et al, 2020). Since 2012, a significant number of newly diagnosed bladder cancer cases, totaling over 118,000, have been reported in 40 European countries, and claimed approximately 52,000 lives during that period(Marcos-Gragera et al, 2015). It is estimated that there were 500,000 new cases of bladder cancer worldwide, resulting in 200,000 deaths in 2020(Lenis et al, 2020a; Richters et al, 2020b). In the United States alone, over 80,000 new cases and 17,000 deaths are attributed to bladder cancer annually(Lenis et al. 2020a; Richters et al. 2020b; Siegel et al. 2023). The uneven geographical distribution of bladder cancer incidence and mortality may be attributed to various influential factors, including geographic and other determinants(Amin et al, 2019). In China, bladder cancer is a prevalent malignancy characterized by high incidence rates and expensive treatment costs(Xu et al. 2021; Wu et al. 2023). Recent updates from the International Agency for Research on Cancer (IARC) revealed a general decrease in incidence and mortality rates across most Western countries, whereas some Eastern European countries and developing nations have experienced an increase in these rates(Chavan et al, 2014). Given the elevated prevalence of bladder cancer and its propensity for recurrence following treatment(Stenzl et al, 2011; Lenis et al, 2020), the burden on health services is enormous and needs to be given high priority(Leal et al, 2016; Wong et al, 2018; Xia et al, 2022).\u003c/p\u003e\u003cp\u003eVarious factors influence the incidence of bladder cancer, with genetic susceptibility playing a significant role(Burger et al, 2013; Rothenberg 2015; Figueroa et al, 2016; Byrne et al, 2023). First-degree relatives of individuals with bladder cancer have a significantly higher risk of developing the disease, which is estimated to be approximately twice as high as that of the general population(Burger et al, 2013). Factors such as slow acetylation may not intrinsically lead to bladder cancer but may confer additional risk to exposure to carcinogens such as tobacco products(Burger et al, 2013). Smoking is the primary and widely recognized risk factor for bladder cancer(Burger et al, 2013; Sanli et al, 2017; Xiong et al, 2022; Seisen et al, 2023). The proportion of smokers among all patients with bladder cancer is about half(Burger et al, 2013). Evidence indicates that alterations in smoking prevalence account for only a portion of the observed changes in bladder cancer incidence, highlighting the multifaceted nature of its etiology(Seisen et al, 2023). Air pollution is related to the incidence and mortality of bladder cancer to some extent (Sanli et al, 2017; Chen et al, 2022). In Taiwan, a matched case-control study showed that the incidence of bladder cancer increases with increasing levels of air pollution(Liu et al, 2009). Sex is a notable determinant of the occurrence of bladder cancer, with men being disproportionately affected compared to women. Men have a notable three to four-fold higher incidence of bladder cancer than women(Dobruch et al, 2016). The incidence of bladder cancer is spatially and temporally heterogeneous, and changes in lifestyle and ecology may be associated with the development of bladder cancer(Smith et al, 2016).\u003c/p\u003e\u003cp\u003eIn many countries and regions, environmental pollution and climate change have increased the burden of disease, including bladder cancer. The comprehensive utilization of spatial analysis technology and traditional epidemiological research methods can more effectively analyze the spatial and temporal distribution pattern of bladder cancer and its related influencing factors (Saint-Jacques et al, 2016). These variations can be divided into two main categories: anthropogenic and natural variations. Anthropogenic factors encompass a range of economic and social aspects, including GDP per capita and availability of medical resources. Additionally, they involve environmental quality factors such as air pollution and land-use practices. On the other hand, natural factors comprise geographic elements such as mean elevation and relief degree of the land surface, as well as climatic factors like rainfall and temperature(Zhang et al, 2023a; Zhang et al, 2023b). The main objective of this study was to investigate potential factors affecting the incidence of bladder cancer in mainland China. First, we characterized the spatial aggregation of bladder cancer using spatial scanning technique. And then, spatial regression model was used to identify the association between bladder cancer and environmental factors. Finally, countermeasures and suggestions for the prevention and treatment of bladder cancer were proposed.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources and Pre-Processing\u003c/h2\u003e\u003cp\u003eThe incidence data of bladder cancer were derived from 'China Cancer Registry Annual Report 2017\u0026ndash;2019'. In the study year, 178 counties (districts) provided data for three consecutive years. In order to eliminate the influence of age structure in different regions on the results, we used the world standardized incidence rate. Air quality data, such as AQI, were acquired from the Atmospheric Composition Analysis Group at Dalhousie University and sourced from NASA satellite measurements. Meteorological data, such as mean annual temperature, mean annual precipitation, and mean annual sunshine hours, were obtained from the National Weather Science Data Center. The socioeconomic data utilized in this study, such as GDP, total grain yield, the ratio of beds in healthcare institutions to every 1000 individuals, and urbanization rate, were collected from the 2014\u0026ndash;2016 to China County Statistical Yearbook. In addition, the mean elevation and degree of relief on the land surface were obtained from the Advanced Satellite-Borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model ( ASTER GDEM) (30 m spatial resolution).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Spatial scan\u003c/h2\u003e\u003cp\u003eSpatial scan statistics were used to analyze spatial patterns of bladder cancer incidence. By calculating the log-likelihood ratio (\u003cem\u003eLLR\u003c/em\u003e) values for all window sizes and locations, the largest log-likelihood ratio value was identified. This value indicates the area where disease aggregation is most likely to occur and least likely to be caused by random variation(Takahashi et al, 2008).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Spatial regression\u003c/h2\u003e\u003cp\u003eSpatial regression analysis can comprehensively address the spatial autocorrelation and heterogeneity present in spatial data. Spatial regression model was use to quantify the associations between the influence factors and bladder cancer incidence(Zhang et al, 2023a; Zhang et al, 2023b). This approach allows for a more effective utilization and analysis of the spatial attributes inherent to such data.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results and analysis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Basic distribution characteristics and gender differences\u003c/h2\u003e\u003cp\u003eWe selected 178 counties (districts) that provided consistent data in the \u0026lsquo;China Tumor Registry Annual Report 2017\u0026ndash;2019\u0026rsquo;. The three-year average land area of these selected districts was 450,868 square kilometers and the average total population was 115,722,356. From 2014 to 2016, 16,001 cases of bladder cancer were reported, of which 12,528 (78.3%) were male and 3,473 (21.7%) were female. The average crude incidence rate of bladder cancer in the 178 tumor registry areas was calculated to be 4.6 cases per 100,000 people. Specifically, regarding the male population, there was a mean incidence rate of 3.6 cases of bladder cancer per 100,000 individuals, whereas for the female population, the mean incidence rate was noted as 1.0/100,000 individuals. Furthermore, when considering the standardized incidence rate, the average rate for males was 4.91, while for females it was 1.31. This analysis was conducted using the SPSS 25 software. The male-to-female sex ratio was calculated to be 4.41 (95% \u003cem\u003eCI\u003c/em\u003e: 4.04\u0026ndash;4.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The spatial distribution of average standardized incidence rate (ASIRW) for men and women with bladder cancer in China from 2014 to 2016 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spatial distribution characteristics\u003c/h2\u003e\u003cp\u003eBy means of spatial scan on the incidence of bladder cancer in men, we identified eight statistically significant clusters, encompassing a total of 112 counties (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the group of regions with high-incidence clusters, the primary cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;270.51, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) comprised Jiangsu and Zhejiang province, consisting of a total of 14 counties (districts). The second cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;213.31, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) encompassed seven counties (districts) in the Liaoning and Shandong province. Within the third cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52.36, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), there were 14 counties (districts) located in Sichuan, Yunnan, and Guizhou Province. The fourth cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.01, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) consisted of nine counties (districts) in Shandong and Jiangsu province. For low incidence clusters, the primary cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;351.00, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) included 30 counties (districts) spread across Shanxi, Hebei, Shaanxi, Henan, and Shandong province. In the second cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;80.09, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 29 counties (districts) were located in Jiangxi, Hunan, Hainan, Fujian, Guangxi, and Guangdong. The third cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41.88, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was observed in two counties (districts) in Sichuan. The fourth cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.44063, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) encompassed seven counties (districts) within Jiangsu Province.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of spatial scanning analysis of incidence of bladder cancer in men in China\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eHigh-mortality cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eLow-mortality cluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadius (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e895.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e168.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e362.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e738.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e120.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e72.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObserved cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpected cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1247.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e419.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e456.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1010.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2385.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1790.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e138.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e826.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLLR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUpon analyzing the incidence of bladder cancer in women, four statistically significant clusters encompassing 94 counties were identified (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two high-incidence clusters were identified in the analysis. The first cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;106.33, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) consisted of six counties (districts) located in southern Jiangsu and Zhejiang province. The second cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72.12, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) spanned 23 counties (districts) in Liaoning, Shandong, Hebei, Jilin, Inner Mongolia, and Heilongjiang province. The primary cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.59, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) comprised 38 counties (districts) situated in Hunan, Hubei, Jiangxi, Henan, Shaanxi, and Sichuan province. The second cluster (\u003cem\u003eLLR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.25, \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) covered 27 counties (districts) located in Shanxi, Hebei, Henan, and Shandong province.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of spatial scanning analysis of incidence of bladder cancer in women in China\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHigh-mortality cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eLow-mortality cluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadius (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1102.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e501.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e378.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObserved cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpected cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e347.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e816.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e545.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLLR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Results of spatial regression\u003c/h2\u003e\u003cp\u003eBy means of spatial regression, the associations between the incidence of bladder cancer and various factors including air quality, climatic conditions, socioeconomic indicators, and geographic factors were identified. The independent variables considered in the analysis included the air quality index (AQI), mean annual temperature, mean annual rainfall, mean annual hours of sunlight, total food production, gross domestic product (GDP), urbanization rate, the number of beds in medical institutions per 1000 people, mean elevation, and the degree of relief on the land surface. The dependent variables were the incidence of bladder cancer in men and women. Results showed that there was a positive association between number of beds in medical institutions per 1000 people (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.334, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the incidence of bladder cancer in men. Conversely, a negative correlation was observed between the air quality index (AQI) (\u003cem\u003eβ\u003c/em\u003e = -0.251, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and mean annual temperature (\u003cem\u003eβ\u003c/em\u003e = -0.416, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the incidence of bladder cancer in men (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of model screening and fitting of bladder cancer incidence and influencing factors in men\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTYPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal food production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05537\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe ratio of beds in health-care institutions to every 1000 individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.36473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual hours of sunlight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.14067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean elevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree of relief in the land surface\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.20858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeanwhile, a positive correlation was observed between the number of beds in medical institutions per 1000 people (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.111, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), mean annual hours of sunlight (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and mean elevation and the degree of relief in the land surface (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.158, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the incidence of bladder cancer in women. Conversely, negative correlations were found between the mean annual temperature (\u003cem\u003eβ\u003c/em\u003e = -0.226, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mean annual rainfall (\u003cem\u003eβ\u003c/em\u003e = -0.102, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the incidence of bladder cancer in women (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of model screening and fitting of bladder cancer incidence and influencing factors in women\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTYPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal food production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.51854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe ratio of beds in health-care institutions to every 1000 individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.63259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual hours of sunlight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean elevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree of relief in the land surface\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Incidence of bladder cancer and gender difference\u003c/h2\u003e\u003cp\u003eThe findings of the present study highlight a notable disparity between men and women in terms of bladder cancer development, with the most prominent risk factor being smoking, potentially explaining this difference(Antoni et al, 2017; Wong et al, 2018; Densmore et al, 2019). It is commonly reported that smoking rates tend to be higher among men compared to women(Chavan et al, 2014; Sanli et al, 2017; Densmore et al, 2019). Based on data provided by the American Cancer Society, individuals who engage in smoking have a substantially increased risk (no less than three times greater) of developing bladder cancer compared with those who do not smoke(Seisen et al, 2023). In a recent meta-analysis of multiple studies, smokers were observed to have a higher risk of developing bladder cancer than former smokers(Van Osch et al, 2019; Seisen et al, 2023). Smoking prevalence alone cannot fully account for the variation in bladder cancer incidence between men and women, indicating that other factors are also at play. It is well recognized that smoking is an important risk factor for bladder cancer, but there may be other determinants that contribute to the observed differences in incidence rates(Krabbe et al, 2015). Hence, while the sex disparity in smoking prevalence can partially account for the variation in bladder cancer incidence between men and women, it is not the sole determining factor (Hemelt et al, 2009; Dobruch et al, 2016). Therefore, in addition to smoking prevalence, bladder cancer incidence is influenced by numerous underlying biological and epidemiological factors(Dobruch et al, 2016). Although men have a higher incidence of bladder cancer, it is important to highlight that women tend to exhibit a higher propensity for advanced-stage disease diagnosis and poorer prognosis(Viswambaram and Hayne 2020). Several factors could potentially explain this disparity, including variations in carcinogenic catabolism, hormonal signaling, and exposure to risk factors. Additionally, delayed diagnosis in women, anatomical differences, and the impact of female cystectomy may also contribute to these differences(Viswambaram and Hayne 2020). Bladder cancer is a prevalent form of cancer in Taiwan, affecting both men and women(Hsiao et al, 2021). According to data from 2012, the age-standardized incidence rates for bladder cancer were reported as 8.70 and 3.34 per 100,000 population for males and females, respectively. These figures highlight the higher incidence rate among males than among females at that time. Looking ahead to 2025, projections indicate an increase in the age-standardized incidence rate for males. It is anticipated to reach 10.4 cases per 100,000 population, reflecting an upward trend. In contrast, for women, the projected age-standardized incidence rate for 2025 is expected to be 3.7 per 100,000 individuals, representing a relatively stable incidence rate within this timeframe(Hsiao et al, 2021). To provide a comprehensive analysis of the incidence of bladder cancer across different regions, we used data extracted from the GLOBOCAN database. This study focused on 32 distinct regions and presented the standardized incidence rates for bladder cancer in 2012, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(Wong et al, 2018). The results of this study showed that there were significant differences in the incidence of bladder cancer among men in different areas. Southern Europe, Western Europe, and North America exhibited the highest incidence rates, surpassing 19 cases per 100,000 people. Conversely, West and Central Africa had the lowest recorded incidence rates at 2.1 and 2.2 cases, respectively. For females, North America and Western Europe had the highest incidence of bladder cancer, reported as 5.1 and 4.3 cases per 100,000 people, respectively. Conversely, Melanesia and South-Central Asia demonstrated the lowest recorded rates, of 0.7 and 0.8 cases, respectively(Wong et al, 2018). Additionally, this study examined the male-to-female prevalence ratio, known as the incidence rate ratio (\u003cem\u003eIRR\u003c/em\u003e), for bladder cancer. Micronesia displayed the highest \u003cem\u003eIRR\u003c/em\u003e (7.22), indicating a higher prevalence of bladder cancer in males. In contrast, West Africa showed the lowest \u003cem\u003eIRR\u003c/em\u003e (1.62), suggesting a relatively lower sex disparity in bladder cancer prevalence within that region. These findings underscore the importance of understanding the regional variations in the incidence of bladder cancer and sex-specific disparities. Such insights are vital for identifying high-risk regions and for designing targeted prevention and early detection strategies. This study investigated the incidence of bladder cancer in 178 counties (districts) in inland China between 2014\u0026ndash;2016. Despite the different time spans from Taiwan and other mentioned regions, the study highlighted that both male and female bladder cancer incidence rates were higher in Taiwan than in 178 counties (districts) in China. Conversely, regions such as West and Central Africa exhibited lower incidence in men and women than in China. The variation in bladder cancer incidence rates across these regions might be associated with the Human Development Index (HDI) of each country(Cumberbatch et al, 2018a; Wong et al, 2018b). Factors encompassed within the HDI, including the quality of healthcare, socioeconomic status, and access to resources, could contribute to differences in disease incidence and outcome. This study showed that the male-to-female incidence ratio in 178 counties (districts) in inland China from to 2014\u0026ndash;2016 was 4.41. This suggests that the prevalence of bladder cancer in males is more than four times higher than that in females, with an even greater disparity when compared to that in West and East Africa.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardized incidence rates of bladder cancer for men and women and ratio of incidence rates for men and women, by region, 2012\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale ASIWW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale ASIWW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eIRR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Eastern Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmerica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEurope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral and Eastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralia/New Zealand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMelanesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicronesia/Polynesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore developed regions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess developed regions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Spatial distribution characteristics of bladder cancer\u003c/h2\u003e\u003cp\u003eThe results of spatial scanning showed that the high incidence clusters of bladder cancer in men and women were mainly clustered in the eastern part of China. One of the reasons may be due to the socioeconomic differences in different regions. This suggests a potential relationship between bladder cancer incidence and socioeconomic status in different regions(Densmore et al, 2019). A study conducted in Canada observed that the incidence of bladder cancer is increasing among socioeconomically disadvantaged populations(Densmore et al, 2019). Similarly, unequal levels of economic development across various regions in China may also play a role in the spatial variability observed in bladder cancer incidence. For the incidence of bladder cancer in men, there was a significant gap between the northern and southern regions of Jiangsu Province(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This discrepancy can likely be attributed to the stronger industrial base, higher urbanization, and greater economic development in southern Jiangsu as well as the presence of more severe air pollution in the region(Cheng and Nathanail 2019). The observed high incidence clustering in certain counties (districts) within Liaoning, Shandong, and Sichuan provinces may be linked to factors such as a larger elderly population, higher level of aging, and accelerated aging process. Notably, Liaoning province ranks first in the country in terms of population aging according to the National Bureau of Statistics, with Sichuan also appearing on the list. It has been suggested that overall growth and aging of the population are major contributing factors to the increased incidence of bladder cancer worldwide(Richters et al, 2020; Zi et al, 2021). Regarding the clustering of high incidence in female bladder cancer, it was observed that the eastern region surpasses the central and western regions, with a concentrated high incidence in economically advanced regions such as northern Zhejiang, southern Jiangsu, and northeastern China. The reasons for the high incidence clustering in northern and southern Zhejiang align with those for male bladder cancer. The high incidence clustering in northeastern China may be attributed to the shifting weight of the elderly population rate (EPR) and elderly dependency ratio (EDR) towards the northeastern region. This shift could potentially increase the economic burden of elderly care in Northeast China. Consequently, future development policies should prioritize socioeconomic growth and the allocation of elderly care resources, particularly in northern China(Man et al, 2021). Future development policies should thus concentrate on fostering socioeconomic growth and ensuring the allocation of resources for elderly care, with a particular emphasis on northern China. Multiple factors account for the disparities in the spatial scan maps of the incidence of bladder cancer incidence in men and women. Smoking, delayed diagnosis in women, anatomical variance, and female cystectomy, as mentioned earlier, all contribute to some extent to the differences in incidence rates between the sexes. Additionally, variations in sex ratios and lifestyle choices across different regions in China may also contribute to spatial disparities in bladder cancer incidence rates between men and women(Byrne et al, 2023). Consequently, these factors are responsible for the spatial disparities observed in the incidence of bladder cancer between males and females. Bladder cancer primarily affects the elderly population aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and exhibits a higher incidence rate. As individuals grow older, the deterioration of vital organ function, weakened immunity, and overall physical fitness are significant contributing factors to the development of bladder cancer. Consequently, these factors increase the complexity of clinical care and impose a greater burden on society(Richters et al, 2020). Hence, it is crucial to enhance the scope of early bladder cancer screening and advocate standardized and consistent clinical care for bladder cancer. These measures are essential to reduce the mortality rate of bladder cancer in China and alleviate the burden on the healthcare system. Furthermore, it is important to objectively and comprehensively examine the relationship between specific factors and bladder cancer incidence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Potential environmental influences on bladder cancer\u003c/h2\u003e\u003cp\u003eIn this study, we observed a negative association between air quality index and the incidence of bladder cancer in men. Nevertheless, it is worth mentioning that air pollution contributes to an increased risk of bladder cancer to some extent(Castano-Vinyals et al, 2008; Zhang et al, 2022; Haghayegh et al, 2023). Further investigations are needed to determine whether the air quality index (AQI) affects bladder cancer susceptibility. Regarding the relationship between elevation and cancer, some ecological studies have found that people living at high altitudes and in places with the highest UV radiation are less likely to develop different types of cancers (Calderon-Gerstein and Torres-Samaniego 2021). However, some scientists have also reported an increased incidence of different types of cancer in the same areas(Calderon-Gerstein and Torres-Samaniego 2021). A study conducted by researchers in 2011 on high altitude and cancer mortality showed no significant difference between altitude and cancer mortality(Ezzati et al, 2012; Calderon-Gerstein and Torres-Samaniego 2021). In the present study, the mean elevation and relief of the land surface were associated with the incidence of bladder cancer in women. The reason may be the increase of UV radiation, reduce of availability of fruits and vegetables, decrease of temperature, low selenium levels in the soil, or contamination with mineral elements such as arsenic, among others, which increases the risk of cancer(Calderon-Gerstein and Torres-Samaniego 2021; Pal et al, 2022). Regarding the relationship between bladder cancer incidence and temperature and precipitation, there are few studies in this area at home and abroad. Several studies have provided evidence suggesting an inverse correlation between Polycyclic Aromatic Hydrocarbons (PAHs) and temperature, which suggests that as temperatures rise, the concentration of PAHs decreases, thereby potentially lowering the associated cancer risk(Khan et al, 2018). This is in line with the results of this study that bladder cancer and temperature were negatively correlated, and the relationship between bladder cancer incidence and precipitation needs to be further explored. In this study, the investigation of the relationship between sunshine hours and cancer revealed interesting findings. In the case of women, there was a negative association, indicating that increased sunshine hours were linked to a lower incidence of bladder cancer. These results raise intriguing questions concerning the potential sex-specific effects of sunshine hours on bladder cancer risk. The underlying mechanisms behind this sex disparity remain unclear and warrant further investigation. Biological factors, such as hormonal differences, may contribute to the varying responses to sunlight exposure between men and women. Additionally, lifestyle behaviors and cultural practices related to sun exposure could also influence the observed associations. It is important to note that while this study suggests a potential link between sunshine hours and bladder cancer incidence in women, it does not establish a causal relationship. Other factors, such as genetic predisposition, dietary patterns, and occupational exposure, need to be considered when comprehensively assessing bladder cancer risk. Recent studies have shed light on the correlation between the incidence of bladder cancer and external factors in men and women. Notably, findings have consistently highlighted a positive relationship between GDP per capita and the occurrence of bladder cancer in both genders(Teoh et al, 2020). This suggests that regions with higher economic status are more prone to an increased risk of bladder cancer. The underlying causes of this association could be multifaceted, encompassing lifestyle choices, exposure to environmental pollutants, or disparities in healthcare access and awareness. In parallel, the number of beds in medical institutions per 1000 people has been recognized as a representative measure of local healthcare resources. Areas equipped with greater healthcare resources often exhibit enhanced medical care provision and more precise screening protocols, contributing to a heightened detection rate of bladder cancer cases. This emphasizes the pivotal role of accessible and well-equipped healthcare facilities in the early diagnosis and timely intervention of patients with bladder cancer. Interestingly, total food production and urbanization rate were not significantly associated with bladder cancer incidence in either sex. These findings indicate that variables related to agricultural productivity and the level of urban development do not directly affect the likelihood of bladder cancer development among men and women. Moreover, the study outcomes underscore the existence of disparities not only in the incidence rates of bladder cancer between men and women but also in the susceptibility of each sex to external influencing factors. Biological, hormonal, and behavioral differences may contribute to these variations, necessitating further research to unravel the underlying mechanisms and tailor prevention strategies accordingly.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this paper, the spatial distribution characteristics of bladder cancer incidence in mainland China were revealed for the first time with county-level administrative units as the spatial resolution, and the effects of economic society, natural conditions and air quality were analyzed. The incidence of bladder cancer was higher in men than in women. The spatial distribution of bladder cancer in mainland China revealed substantial heterogeneity, characterized by concentrated high-risk clusters in the eastern region. Air quality, climatic conditions, and medical level have an impact on the incidence of bladder cancer in men and women. It should be pointed out that this paper is a retrospective study, which is limited to relevance, not causality. Future studies should focus on unraveling the underlying mechanisms driving these spatial epidemiological trends and incorporating the analysis of potential risk factors into comprehensive analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo statement of ethical approval as there are no ethical implications in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll information was de-identified, and we do not need patients\u0026rsquo; informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used or analyzed during the current study are available from the corresponding author\u0026nbsp;upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this\u0026nbsp;study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFounding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;Fujian Provincial Joint Science and Technology Innovation Funding Project (2021Y9117) and\u0026nbsp;Horizontal Scientific Research Project of Wenzhou Medical University (KJHX2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFeidi Li: Methodology, Data curation, Software, Formal analysis, Visualization, Writing-original draft, Writing-review \u0026amp; editing. Jiaxin Liu, Guangyuan Liu, Qi Chen, Shuren Sun: Methodology, Data curation, Writing-review \u0026amp; editing. Mengqi Zhang, Shuhui Huang, Peipei Zhou and Zhonglu Liao: Methodology, Formal analysis, Writing-review \u0026amp; editing. Hong Huang and Zhigang Wu: Idea, Methodology, Resource, Writing-review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmin, R. W., B. Stafford, et al (2019). \"A spatial study of bladder cancer mortality and incidence in the contiguous US: 2000\u0026ndash;2014.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Total Environ\u003c/span\u003e \u003cb\u003e670\u003c/b\u003e: 806\u0026ndash;813.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntoni, S., J. Ferlay, I. Soerjomataram, et al (2017). \"Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e71\u003c/b\u003e(1): 96\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBabjuk, M. (2017). \"Trends in Bladder Cancer Incidence and Mortality: Success or Disappointment?\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e71\u003c/b\u003e(1): 109\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurger, M., J. W. Catto, G. Dalbagni, et al (2013). \"Epidemiology and risk factors of urothelial bladder cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e63\u003c/b\u003e(2): 234\u0026ndash;241.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByrne, S., T. Boyle, M. Ahmed, et al (2023). \"Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Epidemiol\u003c/span\u003e \u003cb\u003e52\u003c/b\u003e(3): 817\u0026ndash;826.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalderon-Gerstein, W. S., G. Torres-Samaniego (2021). \"High altitude and cancer: An old controversy.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRespir Physiol Neurobiol\u003c/span\u003e \u003cb\u003e289\u003c/b\u003e: 103655.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastano-Vinyals, G., K. P. Cantor, N. Malats, et al (2008). \"Air pollution and risk of urinary bladder cancer in a case-control study in Spain.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOccup Environ Med\u003c/span\u003e \u003cb\u003e65\u003c/b\u003e(1): 56\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChavan, S., F. Bray, J. Lortet-Tieulent, et al (2014). \"International variations in bladder cancer incidence and mortality.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e66\u003c/b\u003e(1): 59\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, J., S. Rodopoulou, M. Strak, et al (2022). \"Long-term exposure to ambient air pollution and bladder cancer incidence in a pooled European cohort: the ELAPSE project.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBr J Cancer\u003c/span\u003e \u003cb\u003e126\u003c/b\u003e(10): 1499\u0026ndash;1507.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng, Y. and C. P. Nathanail (2019). \"A study of \"cancer villages\" in Jiangsu Province of China.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEnviron Sci Pollut Res Int\u003c/span\u003e \u003cb\u003e26\u003c/b\u003e(2): 1932\u0026ndash;1946.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCumberbatch, M. G. K., I. Jubber, P. C. Black, et al (2018). \"Epidemiology of Bladder Cancer: A Systematic Review and Contemporary Update of Risk Factors in 2018.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e74\u003c/b\u003e(6): 784\u0026ndash;795.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDensmore, R., M. Hajizadeh, M. Hu (2019). \"Trends in socio-economic inequalities in bladder cancer incidence in Canada: 1992\u0026ndash;2010.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCan J Public Health\u003c/span\u003e \u003cb\u003e110\u003c/b\u003e(6): 722\u0026ndash;731.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobruch, J., S. Daneshmand, M. Fisch, et al (2016). \"Gender and Bladder Cancer: A Collaborative Review of Etiology, Biology, and Outcomes.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e69\u003c/b\u003e(2): 300\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEzzati, M., M. E. Horwitz, D. S. Thomas, et al (2012). \"Altitude, life expectancy and mortality from ischaemic heart disease, stroke, COPD and cancers: national population-based analysis of US counties.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Epidemiol Community Health\u003c/span\u003e \u003cb\u003e66\u003c/b\u003e(7): e17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerlay, J., H. R. Shin, F. Bray, et al (2010). \"Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Cancer\u003c/span\u003e \u003cb\u003e127\u003c/b\u003e(12): 2893\u0026ndash;2917.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFigueroa, J. D., C. D. Middlebrooks, A. R. Banday, et al (2016). \"Identification of a novel susceptibility locus at 13q34 and refinement of the 20p12.2 region as a multi-signal locus associated with bladder cancer risk in individuals of European ancestry.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHum Mol Genet\u003c/span\u003e \u003cb\u003e25\u003c/b\u003e(6): 1203\u0026ndash;1214.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaghayegh, S., Y. Liu, Y. Zhang, et al (2023). \"Rotating Night Shift Work and Bladder Cancer Risk in Women: Results of Two Prospective Cohort Studies.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Environ Res Public Health\u003c/span\u003e \u003cb\u003e20\u003c/b\u003e(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe, H., H. Xie, Y. Chen, et al (2020). \"Global, regional, and national burdens of bladder cancer in 2017: estimates from the 2017 global burden of disease study.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBMC Public Health\u003c/span\u003e \u003cb\u003e20\u003c/b\u003e(1): 1693.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemelt, M., H. Yamamoto, K. K. Cheng, et al (2009). \"The effect of smoking on the male excess of bladder cancer: a meta-analysis and geographical analyses.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Cancer\u003c/span\u003e \u003cb\u003e124\u003c/b\u003e(2): 412\u0026ndash;419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsiao, B. Y., S. Y. Su, J. R. Jhuang, et al (2021). \"Ensemble forecasting of a continuously decreasing trend in bladder cancer incidence in Taiwan.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Rep\u003c/span\u003e \u003cb\u003e11\u003c/b\u003e(1): 8373.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan, M. B., M. Masiol, C. Bruno, et al (2018). \"Potential sources and meteorological factors affecting PM(2.5)-bound polycyclic aromatic hydrocarbon levels in six main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEnviron Sci Pollut Res Int\u003c/span\u003e \u003cb\u003e25\u003c/b\u003e(32): 31987\u0026ndash;32000.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrabbe, L. M., R. S. Svatek, S. F. Shariat, et al (2015). \"Bladder cancer risk: Use of the PLCO and NLST to identify a suitable screening cohort.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eUrol Oncol\u003c/span\u003e \u003cb\u003e33\u003c/b\u003e(2): 65 e19-25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeal, J., R. Luengo-Fernandez, R. Sullivan, et al (2016). \"Economic Burden of Bladder Cancer Across the European Union.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e69\u003c/b\u003e(3): 438\u0026ndash;447.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenis, A. T., P. M. Lec, K. Chamie, et al (2020). \"Bladder Cancer: A Review.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJAMA\u003c/span\u003e \u003cb\u003e324\u003c/b\u003e(19): 1980\u0026ndash;1991.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, C. C., S. S. Tsai, H. F. Chiu, et al (2009). \"Ambient exposure to criteria air pollutants and risk of death from bladder cancer in Taiwan.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInhal Toxicol\u003c/span\u003e \u003cb\u003e21\u003c/b\u003e(1): 48\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMan, W., S. Wang, H. Yang (2021). \"Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBMC Public Health\u003c/span\u003e \u003cb\u003e21\u003c/b\u003e(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcos-Gragera, R., S. Mallone, L. A. Kiemeney, et al (2015). \"Urinary tract cancer survival in Europe 1999\u0026ndash;2007: Results of the population-based study EUROCARE-5.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur J Cancer\u003c/span\u003e \u003cb\u003e51\u003c/b\u003e(15): 2217\u0026ndash;2230.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePal, L., T. Jenei, M. McKee, et al (2022). \"Health and economic gain attributable to the introduction of the World Health Organization's drinking water standard on arsenic level in Hungary: A nationwide retrospective study on cancer occurrence and ischemic heart disease mortality.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Total Environ\u003c/span\u003e \u003cb\u003e851\u003c/b\u003e(Pt 2): 158305.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichters, A., K. K. H. Aben, L. Kiemeney (2020). \"The global burden of urinary bladder cancer: an update.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWorld J Urol\u003c/span\u003e \u003cb\u003e38\u003c/b\u003e(8): 1895\u0026ndash;1904.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRothenberg, R. (2015). \"The causes of cancer, revisited.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnn Epidemiol\u003c/span\u003e \u003cb\u003e25\u003c/b\u003e(3): 215\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaint-Jacques, N., J. S. W. Lee, P. Brown, et al (2016). \"Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBMC Public Health\u003c/span\u003e \u003cb\u003e16\u003c/b\u003e: 175.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanli, O., J. Dobruch, M. A. Knowles, et al (2017). \"Bladder cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Rev Dis Primers\u003c/span\u003e \u003cb\u003e3\u003c/b\u003e: 17022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeisen, T., M. Labban, S. R. Lipsitz, et al (2023). \"Assessment of the Ecological Association between Tobacco Smoking Exposure and Bladder Cancer Incidence over the Past Half-Century in the United States.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCurr Oncol\u003c/span\u003e \u003cb\u003e30\u003c/b\u003e(2): 1986\u0026ndash;1998.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., K. D. Miller, N. S. Wagle, et al (2023). \"Cancer statistics, 2023.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCA Cancer J Clin\u003c/span\u003e \u003cb\u003e73\u003c/b\u003e(1): 17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, N. D., S. M. Prasad, A. R. Patel, et al (2016). \"Bladder Cancer Mortality in the United States: A Geographic and Temporal Analysis of Socioeconomic and Environmental Factors.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Urol\u003c/span\u003e \u003cb\u003e195\u003c/b\u003e(2): 290\u0026ndash;296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStenzl, A., N. C. Cowan, M. De Santis, et al (2011). \"Treatment of muscle-invasive and metastatic bladder cancer: update of the EAU guidelines.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e59\u003c/b\u003e(6): 1009\u0026ndash;1018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakahashi, K., M. Kulldorff, T. Tango, et al (2008). \"A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Health Geogr\u003c/span\u003e \u003cb\u003e7\u003c/b\u003e: 14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeoh, J. Y., J. Huang, W. Y. Ko, et al (2020). \"Global Trends of Bladder Cancer Incidence and Mortality, and Their Associations with Tobacco Use and Gross Domestic Product Per Capita.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEur Urol\u003c/span\u003e \u003cb\u003e78\u003c/b\u003e(6): 893\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Osch, F. H. M., J. Vlaanderen, S. H. J. Jochems, et al (2019). \"Modeling the Complex Exposure History of Smoking in Predicting Bladder Cancer: A Pooled Analysis of 15 Case-Control Studies.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEpidemiology\u003c/span\u003e \u003cb\u003e30\u003c/b\u003e(3): 458\u0026ndash;465.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eViswambaram, P., D. Hayne (2020). \"Gender discrepancies in bladder cancer: potential explanations.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExpert Review of Anticancer Therapy\u003c/span\u003e \u003cb\u003e20\u003c/b\u003e(10): 841\u0026ndash;849.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong, M. C. S., F. D. H. Fung, C. Leung, et al (2018). \"The global epidemiology of bladder cancer: a joinpoint regression analysis of its incidence and mortality trends and projection.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Rep\u003c/span\u003e \u003cb\u003e8\u003c/b\u003e(1): 1129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, R., X. Teng, Q. Song, et al (2023). \"Single-cell RNA sequencing reveals sexual diversity in the human bladder and its prospective impacts on bladder cancer and urinary tract infection.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBMC Med Genomics\u003c/span\u003e \u003cb\u003e16\u003c/b\u003e(1): 122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia, C., X. Dong, H. Li, M. Cao, et al (2022). \"Cancer statistics in China and United States, 2022: profiles, trends, and determinants.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChin Med J (Engl)\u003c/span\u003e \u003cb\u003e135\u003c/b\u003e(5): 584\u0026ndash;590.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong, J., L. Yang, Y. Q. Deng, et al (2022). \"The causal association between smoking, alcohol consumption and risk of bladder cancer: A univariable and multivariable Mendelian randomization study.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Cancer\u003c/span\u003e \u003cb\u003e151\u003c/b\u003e(12): 2136\u0026ndash;2143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, Y., C. Luo, J. Wang, et al (2021). \"Application of nanotechnology in the diagnosis and treatment of bladder cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Nanobiotechnology\u003c/span\u003e \u003cb\u003e19\u003c/b\u003e(1): 393.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, H. W., Z. R. Tsai, V. C. Kok, et al (2022). \"Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Rep\u003c/span\u003e \u003cb\u003e12\u003c/b\u003e(1): 20799.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, M., X. Dai, G. Chen, et al (2023). \"Analysis of the distribution characteristics of prostate cancer and its environmental factors in China.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEnviron Sci Pollut Res Int\u003c/span\u003e \u003cb\u003e30\u003c/b\u003e(11): 29349\u0026ndash;29368.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, M., X. Dai, G. Chen, et al (2023). \"The Association between Spatial-Temporal Distribution of Prostate Cancer and Environmental Factors in Mainland China.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancer Epidemiol Biomarkers Prev\u003c/span\u003e \u003cb\u003e32\u003c/b\u003e(2): 208\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZi, H., S. H. He, X. Y. Leng, et al (2021). \"Global, regional, and national burden of kidney, bladder, and prostate cancers and their attributable risk factors, 1990\u0026ndash;2019.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMil Med Res\u003c/span\u003e \u003cb\u003e8\u003c/b\u003e(1): 60.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental pollution, Climate change, Spatial scan, Spatial regression, Association","lastPublishedDoi":"10.21203/rs.3.rs-8143352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8143352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e[Background]\u003c/strong\u003e Under the dual pressures of environmental pollution and climate change, the high incidence of bladder cancer has become a major public health problem in many countries and regions, and the disease burden is increasing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Objective]\u003c/strong\u003e To analyze the spatial distribution patterns of the incidence of bladder cancer in China, and to identify the influences of socioeconomic, ecological, and meteorological factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Methods]\u003c/strong\u003e The incidence of bladder cancer in 178 county-level administrative units was obtained from 'China Cancer Registry Annual Report 2017–2019', and data on air quality, meteorological conditions and economic development during the same period were collected through online public channels. Spatial scan statistics were used to analyze spatial patterns of bladder cancer incidence. Spatial regression model was use to identify the associations between the influence factors and bladder cancer incidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Results]\u003c/strong\u003e The incidence of bladder cancer in men was 4.41 times to women. For men, there are regional differences in the incidence of bladder cancer, and the eastern region is significantly higher than the central region. For women, the incidence of bladder cancer in the eastern region is higher than that in the central and western regions. For men, number of beds in medical institutions per 1000 people was positively correlated with the incidence of bladder cancer, while air quality index (AQI) and annual average temperature were negatively correlated. For women, number of beds in medical institutions per 1000 people, the average annual sunshine hours, the average altitude and the topographic relief of the land surface were positively correlated with the incidence of bladder cancer, while average annual temperature and annual rainfall were negatively correlated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Conclusions]\u003c/strong\u003e The incidence of bladder cancer was higher in men than in women. Significant spatial heterogeneity exists in the distribution of bladder cancer in China, with high-risk clusters found in the eastern region. Air quality, climatic conditions, and medical level have an impact on the incidence of bladder cancer in men and women.\u003c/p\u003e","manuscriptTitle":"Spatial distribution characteristics and influencing factors of bladder cancer in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 13:42:01","doi":"10.21203/rs.3.rs-8143352/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af6716a4-38a8-4502-aecb-1257a316b416","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T04:54:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 13:42:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8143352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8143352","identity":"rs-8143352","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}