Residential ultraviolet radiation and breast cancer risk in a large prospective cohort

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Understanding the role of environmental and socioeconomic factors in the geographic variation of breast cancer risk in the US-wide Sister Study

OBJECTIVE

To describe the geographic pattern of breast cancer incidence in a nationwide prospective cohort and investigate whether environmental exposures and/or neighborhood socioeconomic status explain observed geographic disparities.

METHODS

Using accelerated failure time models with a spatial random effect term, we mapped the health region-level association between residential location and breast cancer incidence for 44,707 participants in the Sister Study after controlling for established individual-level breast cancer risk factors. We performed a variable selection process to select environmental exposures [i.e., ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5), PM2.5 chemical composition, outdoor light at night (LAN), ambient noise, ultraviolet radiation, and greenspace] and neighborhood-level factors [i.e., population density and area deprivation index (ADI)] that predicted breast cancer incidence and quantified the spatial variation explained by the selected factors. We also considered whether the geographic pattern and predictors were similar when restricting to estrogen receptor-positive (ER+) tumors.

RESULTS

We observed a spatial patterning in the incidence of overall breast cancer (Moran's I = 16.7, p < 0.05) and ER+ breast cancer (Moran's I = 13.2, p < 0.05), with a lower risk observed in the South and Southeast and a greater risk in the Northwest and certain areas of the Midwest and Northeast. NO2, LAN, and ADI explained 21.4% of the spatial variation in overall breast cancer incidence whereas NO2, PM2.5 chemical composition, LAN, greenspace, and ADI together explained 63.3% of the spatial variation in ER+ breast cancer incidence.

CONCLUSIONS

Our findings provide additional evidence for a role of environmental exposures in breast cancer incidence and suggest that geographic-based risk factors may vary according to breast cancer subtype. Our findings support the need for additional research to quantify the relative contributions of geographic-based risk factors for breast cancer.

Workplace exposure to UV radiation and strategies to minimize cancer risk

BACKGROUND

Workplace exposure to solar ultraviolet (UV) causes malignant melanoma and non-melanoma skin cancer. The evidence for beneficial effects of solar UV exposure in reducing the risks for other cancers is increasing. The intensity of UV radiation at the Earth's surface is dependent on latitude, but even in northern European countries exposure can be high enough for outdoor work to cause skin cancer.

GROWING POINTS

Awareness of the health risks and benefits of occupational solar UV exposure is poor. Actions to reduce the risk of skin cancer have been identified and employers should recognize their responsibility to actively manage these risks. There is evidence for reduced risks for breast, ovarian and colorectal cancer and possibly other cancers linked to solar UV exposure.

SOURCES OF DATA

This narrative review draws on published scientific articles and material designed to assist identifying strategies to protect workers from solar UV exposure.

AREAS OF AGREEMENT

Solar UV exposure can be harmful. Wavelengths in the UVB range are more effective in causing erythema and DNA damage. Solar UV is the main source of vitamin D for most people. Primary and secondary prevention for skin cancer can potentially eliminate these risks but the evidence for effectiveness is limited.

AREAS OF CONTROVERSY

Potential health benefits of UV exposure, particularly for reduced cancer risk. Determining and communicating optimal exposure to maximize health benefits. The risk of non-melanoma skin cancers may be more than doubled for some workers in temperate latitudes.

AREAS TIMELY FOR DEVELOPING RESEARCH

Exposure-response epidemiological studies; studies of the health benefits of occupational UV exposure; studies of the effectiveness of intervention strategies to prevent skin cancer. Use of low-cost UV sensors in workplaces.