Low breast cancer screening prevalence tied to socioeconomic factors – AuntMinnie

Report on Geographic and Socioeconomic Disparities in U.S. Cancer Screening and Implications for Sustainable Development Goals

A study published in JAMA Network Open reveals persistent geographic and socioeconomic disparities in cancer screening rates across the United States, highlighting significant challenges to achieving key Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities).

Study Overview and Methodology

Researchers from the Dana-Farber Cancer Institute analyzed county-level data from 1997 to 2019 for breast, cervical, and colorectal cancer screenings across 3,142 U.S. counties. The study employed spatial analysis to identify geographic patterns and their correlation with sociodemographic factors, providing critical insight into the equitable delivery of preventative healthcare services as mandated by SDG 3.

Analysis of Findings in Relation to SDGs

Overall Trends and Progress Towards SDG 3

The study observed a positive trend of increasing overall screening participation and a reduction in global spatial autocorrelation over time. The distribution of mammography screening, for instance, became 83% more uniform between 1997 and 2019. This suggests progress in reducing geographic disparities and advancing Target 3.4 of SDG 3, which aims to reduce premature mortality from non-communicable diseases through prevention and treatment.

Persistent Disparities and Challenges to SDG 10

Despite overall improvements, the research identified persistent clusters of low and high screening prevalence, directly challenging the aims of SDG 10 (Reduced Inequalities). These geographic inequalities in health access and outcomes demonstrate a failure to ensure equal opportunity for all.

• High-Prevalence Clusters: Consistently high screening rates were observed in the Northeastern U.S., including Maine, New Hampshire, Vermont, and Massachusetts.
• Low-Prevalence Clusters: Consistently low screening participation was concentrated in the Southwest, particularly in Texas, New Mexico, and Arizona.

Socioeconomic Determinants and Their Impact on SDGs

The report establishes a clear link between low screening rates and socioeconomic disadvantage, underscoring how poverty and inequality create significant barriers to healthcare access. This connection impacts multiple SDGs.

1. SDG 10 (Reduced Inequalities): Counties with consistently low screening rates exhibited greater socioeconomic disadvantages compared to high-screening counties. For example, the mean county-level median home value was significantly lower in low-screening clusters ($74,370) than in high-screening clusters ($111,820). This demonstrates a direct correlation between economic status and access to life-saving preventative health services.
2. SDG 3 (Good Health and Well-being): The study highlights that factors such as lower educational attainment, lower incomes, and lower insurance coverage in disadvantaged areas directly impede access to cancer screening. These barriers prevent the realization of universal health coverage and equitable health outcomes for all citizens.
3. SDG 1 (No Poverty) & SDG 11 (Sustainable Cities and Communities): The research points to structural barriers prevalent in disadvantaged and rural areas, such as inadequate transportation and a lack of health facilities. These infrastructure gaps, linked to both poverty and community planning, exacerbate health disparities and hinder progress towards creating inclusive and resilient communities with equitable access to essential services.

Conclusion and Future Directives

While progress has been made in increasing cancer screening uniformity, significant disparities tied to geography and socioeconomic status persist. These inequalities represent a substantial obstacle to achieving the universal health and equality targets outlined in the Sustainable Development Goals. The study calls for future research to incorporate county-level healthcare access characteristics to better understand and formulate policies that address these systemic barriers. Achieving SDG 3 and SDG 10 requires targeted interventions that dismantle the socioeconomic and structural impediments preventing equitable access to preventative cancer care for all populations.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 3: Good Health and Well-being
   • The article’s central theme is cancer screening (mammography, cervical, and colorectal), which is a critical component of public health and preventative medicine. It directly addresses the challenge of ensuring healthy lives by focusing on the early detection of non-communicable diseases (cancer). The study’s findings on low and variable screening rates point to gaps in healthcare services that impact community well-being.
2. SDG 10: Reduced Inequalities
   • The article explicitly links low cancer screening rates to “socioeconomic disadvantages” and geographic location. It highlights significant disparities between different regions of the U.S. (Northeast vs. Southwest) and between counties with different socioeconomic profiles (e.g., lower median home value, lower incomes, lower educational attainment). This directly relates to the goal of reducing inequalities in access to opportunities and services, in this case, essential healthcare.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
   • Cancer is a leading non-communicable disease. The article’s focus on improving cancer screening is a direct measure of “prevention and treatment.” By identifying areas with low screening prevalence, the research highlights where efforts are needed to reduce premature mortality from cancer.
2. Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.
   • The article points to barriers that prevent universal access to essential healthcare services like cancer screening. It mentions factors such as “transportation barriers,” “lack of health facilities in rural areas,” “lower incomes, and lower insurance coverage,” all of which are obstacles to achieving the universal health coverage described in this target.
3. Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
   • The study’s finding that “consistently low [screening] clusters were found in the Southwest” and that these areas are characterized by “lower socioeconomic status” demonstrates a lack of inclusion for these populations in essential health services. Addressing these disparities is necessary to promote the economic and social inclusion of these communities.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. County-level cancer screening prevalence:
   • The article explicitly uses “county-level cancer screening prevalence for breast, cervical, and colorectal cancer” as its primary data. This serves as a direct indicator for measuring access to essential preventative health services (Target 3.8) and progress in preventing non-communicable diseases (Target 3.4).
2. Spatial autocorrelation of screening rates (Global Moran’s I):
   • The study uses the “Global Moran I” analysis to measure geographic clustering. The article states that a decline in this score “suggest[s] increasing screening in later years reduced geographic disparities.” This statistical measure serves as a sophisticated indicator for tracking progress in reducing geographic inequalities (Target 10.2) in healthcare access over time.
3. Socioeconomic factors associated with screening clusters:
   • The article implies that metrics like “county-level median home value,” income levels, and insurance coverage rates can be used as indicators. By correlating these factors with screening rates, one can measure the extent of socioeconomic inequality in health outcomes. The article notes that low-screening counties had a lower mean home value ($74,370.45) compared to high-screening counties ($111,820.41), making this a quantifiable indicator of disparity (Target 10.2).

Summary Table of SDGs, Targets, and Indicators

Source: auntminnie.com