Wide Variation Seen in County-Level Breast Cancer Mortality Linked to SDOH

Although social determinants of health (SDOH) help to provide important data that guide geographically targeted interventions for breast cancer, results of a new cross-sectional study published in JAMA Network Open show that public health efforts could benefit from interventions that are more comprehensive and better geographically targeted than available at present.

SDOH Social Determinants of Health | Image Credit: STOATPHOTO - stock.adobe.com

Whereas current assumptions on associations between breast cancer and SDOH deem the effects on mortality to be static in a geographic area, and when scaled for that area, the authors of the current study wanted to drill down to these outcomes on the county level, and to do so, they used population demographics, environment and lifestyle factors, and health care access to find correlations with breast cancer mortality. Ultimately, they found “access to factors in the built environment to support a healthy lifestyle had varying associations with mortality based on the county in which an individual lives.”

Surveillance, Epidemiology, and End Results data on female patients with breast cancer were collected and adjusted mortality rates for 2015 through 2019 were used to conduct statistical and spatial analyses for 2176 US counties; these data were analyzed in July 2022. Alaska and Hawaii were excluded from the analyses. For this study, breast cancer mortality was defined as annual deaths per 100,000 women per year.

Significant positive associations were found between obesity and county-level age-adjusted mortality rates using multivariable linear regression (ordinary least squares [OLS]) and multiscale geographically weighted regression (MGWR):

• OLS: β = 1.21 (95% CI, 0.88-1.54; P < .001)
• Mean (SD) MGWR: β = 0.72 (0.02)

There were negative associations with mammographic screening:

• OLS: β = −1.27 (95% CI, −1.70 to −0.84; P < .001)
• Mean MGWR: β = −1.07 (0.16)

However, for both obesity and mammographic screening, the overall effects were stationary across the United States, in that 100% of US counties demonstrated a significant association between obesity and mammography and breast cancer mortality.

The story was different for several additional county-level determinants connected to breast cancer mortality. Negative associations were seen between smoking, food environment index, exercise opportunities, racial segregation, mental health care physician ratio, and primary care physician ratio, by both OLS and MGWR:

• Smoking:
  • OLS: β = −0.65 (95% CI, −0.98 to −0.32; P < .001)
  • MGWR: β = −0.75 (0.92)
• Food environment index:
  • OLS: β = −1.35 (95% CI, −1.72 to −0.98; P < .001)
  • MGWR: β = −1.69 (0.70)
• Exercise opportunities:
  • OLS: β = −0.56 (95% CI, −0.91 to −0.21; P = .002)
  • MGWR: β =−0.59 (0.81)
• Racial segregation:
  • OLS: β = −0.60 (95% CI, −0.89 to −0.31; P < .001)
  • MGWR: β = −0.47 (0.41)
• Mental health care physician ratio:
  • OLS: β = −0.93 (95% CI, −1.44 to −0.42; P < .001)
  • MGWR: β = −0.48 (0.92)
• Primary care physician ratio:
  • OLS: β = −1.46 (95% CI, −2.13 to −0.79; P < .001)
  • MGWR: β = −1.06 (0.57)

Percentagewise, significant associations seen for mental health care physician ratio in 14.0% of counties, smoking in 16.3%, racial segregation in 22.6%, primary care physician ratio in 40.6%, and food environment index in 80.3%.

Light pollution was the only remaining SDOH to have a positive association with breast cancer mortality:

• OLS: β, 0.48 (95% CI, 0.24-0.72)
• Mean MGWR: β, 0.27 (0.04)

“Biological and behavioral determinants of breast cancer mortality are generally known and have guided successful interventions and prevention programs that target individuals at risk,” the study authors wrote. “However, due to the complex interrelation between individual and contextual determinants, geographic disparities in breast cancer mortality remain difficult to address.”

Two county groupings with high-cluster incidence of breast cancer mortality rates extended from (1) Kansas to Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, and Georgia and then up through South and North Carolina to Virginia, and (2) the borders of Kentucky, West Virginia, and Ohio. California, Arizona, must of the Northeast, and parts of the Midwest had clusters of counties with low breast cancer mortality.

“To our knowledge, this is the first study applying an MGWR model to assess how associations between breast cancer mortality and county-level social determinants vary across space and scale in the United States,” the authors concluded. “The MGWR approach proposed brought a novel perspective for capturing the spatial interrelations between individuals and contextual factors on a large geographic scale. As suggested by our analysis, this approach may have an unparalleled ability to identify vulnerable populations and geographic areas where targeted interventions may lead to healthier communities.”

Reference

Anderson T, Herrera D, Mireku F, et al. Geographical variation in social determinants of female breast cancer mortality across US counties. JAMA Netw Open. 2023;6(9):e2333618. doi:10.1001/jamanetworkopen.2023.33618