Title: Exploring the Link Between Breast Cancer Mortality and Obesity, Access to Mammograms, and Other Factors
Introduction:
Breast cancer mortality rates in the United States have shown spatial disparities, prompting researchers to investigate the underlying contextual and environmental variables that contribute to such variations. A recent study published in JAMA Network Open explores the association between population demographics, lifestyle factors, access to healthcare, and breast cancer mortality. The study highlights the role of obesity and access to mammograms in influencing breast cancer outcomes. Additionally, it sheds light on various other factors that are differentially linked to breast cancer mortality across different regions of the country.
Understanding the Impact of Obesity and Access to Mammograms:
The study utilized data from the Surveillance, Epidemiology, and End Results database to conduct a geospatial cross-sectional analysis of breast cancer mortality in 2,176 U.S. counties. The findings revealed a significant positive association between obesity and breast cancer mortality rates at the county level. In contrast, a negative association was observed between the proportion of adults screened by mammography and breast cancer mortality rates. These results were consistent across both multivariable linear regression and multiscale geographically weighted regression models.
Other Factors Influencing Breast Cancer Mortality:
The study also identified several other variables that showed significant associations with breast cancer mortality rates. Smoking, a higher dietary environmental index, limited exercise opportunities, racial segregation, and certain proportions of physicians were all negatively linked to breast cancer mortality. On the other hand, light pollution exhibited a positive association with breast cancer mortality.
Geographic Disparities and Regional Variations:
The geographically weighted multiscale regression model revealed significant variation in the magnitude of the effects across different geographic regions in the U.S. This suggests that the impact of certain factors on breast cancer mortality can vary depending on the location. Identifying these regional disparities provides an opportunity to target interventions and resources to vulnerable communities.
Exploring Disability as a Contributing Factor:
Interestingly, while the multivariable linear regression model did not find disability to be a significant variable in breast cancer mortality, the geographically weighted multiscale regression model uncovered a significant positive association in certain regions. This highlights the importance of considering different statistical approaches to gain a comprehensive understanding of factors affecting breast cancer outcomes.
Implications and Potential Interventions:
The study underscores the potential of multiscale geographically weighted regression as a powerful tool for identifying vulnerable populations and geographic areas that require targeted interventions. By better understanding the contextual and environmental variables associated with breast cancer mortality, healthcare professionals and policymakers can develop strategies to promote healthier communities.
Additional Insights: The Importance of Preventive Measures and Education
Breast cancer is a complex disease influenced by a multitude of factors. While this study provides valuable insights, it is essential to consider the role of preventive measures and education. Promoting healthy lifestyles, encouraging regular exercise, and raising awareness about the importance of mammograms can significantly impact breast cancer mortality rates. Community-level initiatives can play a vital role in addressing social determinants of health and improving breast cancer outcomes.
Conclusion:
The study findings reinforce the association between obesity, access to mammograms, and breast cancer mortality rates. They also shed light on other factors such as smoking, dietary environmental index, exercise opportunities, racial segregation, and provider ratios that influence breast cancer outcomes. Understanding the geographic variations and contextual factors associated with breast cancer mortality can guide targeted interventions and resource allocation to reduce disparities and improve overall health outcomes. Further research is warranted to explore the interplay between these factors and develop comprehensive strategies for breast cancer prevention and treatment.
Summary:
A recent study published in JAMA Network Open explores the link between obesity, access to mammograms, and breast cancer mortality rates. The study highlights the significance of obesity and limited access to mammograms as risk factors for breast cancer mortality. Additionally, it identifies several other factors, including smoking, dietary environmental index, exercise opportunities, racial segregation, and provider ratios, that impact breast cancer outcomes. The study emphasizes the need for targeted interventions and resources in geographic areas with high breast cancer mortality rates. Furthermore, it underscores the importance of preventive measures and education to promote healthy lifestyles and improve breast cancer outcomes.
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October 11, 2023
2 minutes of reading
Source/Disclosures
Disclosures: Anderson does not report any relevant financial disclosures. Please see the study for relevant financial disclosures of all other authors.
Key takeaways:
- Obesity and access to mammograms were linked to breast cancer mortality.
- Smoking, dietary environmental index, exercise, racial segregation, and certain proportions of physicians were negatively associated with mortality.
County-level age-adjusted breast cancer death rates were higher among women with less access to mammograms and also among women with obesity, according to study results published in JAMA Network open.
“The spatial heterogeneity of breast cancer mortality in the US presents an opportunity to explore the contextual and environmental variables that could give rise to such spatial disparities and the potential for non-stationarity in these data at different spaces and scales” . Taylor Anderson, PhD, wrote an assistant professor in the department of geography and geoinformation sciences at George Mason University and colleagues. “One such approach, multiscale geographically weighted regression, is an extension of geographically weighted regression that allows the association between determinants and breast cancer mortality to vary both across geographic space and at different scales.”
Anderson and colleagues conducted a geospatial cross-sectional study using data from the Surveillance, Epidemiology, and End Results database of adult women with breast cancer. The researchers identified county-level geographic variations in the associations between population demographics, environment, lifestyle, and access to health care with breast cancer mortality in 2,176 U.S. counties. Researchers also used multivariable linear regression and multiscale geographically weighted regression to understand the impact and importance of variables across the board. US Geographic Regions
Both multivariable linear regression and multiscale geographically weighted regression models showed that county-level age-adjusted models breast cancer mortality The rates were significantly associated positively with obesity (beta = 1.21 and 0.72, respectively) and negatively with the proportion of adults screened by mammography (beta = –1.27 and –1.07, respectively), the researchers wrote. researchers.
The geographically weighted multiscale regression model demonstrated that obesity and access to mammograms were associated with a stationary effect on breast cancer mortality in the US. This model also provided researchers with information on other county-level factors. differentially linked to breast cancer mortality in the US.
Both multivariable linear regression (OLS) and multiscale geographically weighted regression (WGWR) models showed that the following were negatively associated with breast cancer mortality:
- smoking (OLS: beta, –0.65; WGMR: beta, –0.75);
- food environmental index (OLS: beta, –1.35; WGMR: beta, –1.69);
- exercise opportunities (OLS: beta, –0.56; WGMR: beta, –0.59);
- racial segregation (OLS: beta, –0.6; WGMR: beta, –0.47);
- proportion of mental health care physicians (OLS: beta, –0.93; WGMR: beta, –0.48); and
- primary care physician ratio (OLS: beta, –1.46; WGMR: beta, –1.06).
Furthermore, light pollution was positively associated with breast cancer mortality for both the multivariable linear regression model and the multiscale geographically weighted regression model (beta = 0.48 and 0.27, respectively).
The geographically weighted multiscale regression model showed significant variation in the magnitude of effect sizes between geographic regions of the US. The multivariable linear regression model showed that disability is not a significant variable of mortality from breast cancer. However, the geographically weighted multiscale regression model found a significant positive association with disability in some US regions.
“As our analysis suggests, this approach may have an unparalleled ability to identify vulnerable populations and geographic areas where targeted interventions can lead to healthier communities,” the researchers wrote.
https://www.healio.com/news/womens-health-ob-gyn/20231010/us-breast-cancer-mortality-rates-negatively-linked-to-countylevel-mammogram-access
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