Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Exploring the geospatial epidemiology of breast cancer in Iran: identifying significant risk factors and spatial patterns for evidence-based prevention strategies

BACKGROUND

Breast Cancer (BC) is a formidable global health challenge, and Iran is no exception, with BC accounting for a significant proportion of women's malignancies. To gain deeper insights into the epidemiological characteristics of BC in Iran, this study employs advanced geospatial techniques and feature selection methods to identify significant risk factors and spatial patterns associated with BC incidence.

METHODS

Using rigorous statistical methods, geospatial data from Iran, including cancer-related, sociodemographic, healthcare infrastructure, environmental, and air quality data at the provincial level, were meticulously analyzed. Age-standardized incidence rates (ASR) are calculated, and different regression models are used to identify significant variables associated with BC incidence. Spatial analysis techniques, including global and local Moran's index, geographically weighted regression, and Emerging hotspot analysis, were utilized to examine geospatial patterns, identify clustering and hotspots, and assess spatiotemporal distribution of BC incidence.

RESULTS

The findings reveal that BC predominantly affects women (98.03%), with higher incidence rates among those aged 50 to 79. Isfahan (ASR = 26.1) and Yazd (ASR = 25.7) exhibit the highest rates. Significant predictors of BC incidence, such as marriage, tertiary education attainment rate, physician-to-population ratio, and PM2.5 air pollution, are identified through regression models.

CONCLUSION

The study's results provide valuable information for the development of evidence-based prevention strategies to reduce the burden of BC in Iran. The findings underscore the importance of early detection, health education campaigns, and targeted interventions in high-risk clusters and adjacent regions. The geospatial insights generated by this study have implications for policy-makers, researchers, and public health practitioners, facilitating the formulation of effective BC prevention strategies tailored to the unique epidemiological patterns in Iran.