Background: There is extensive interest in understanding how neighborhood socioeconomic status (nSES) may affect cancer incidence or survival. However, variability regarding items included and approaches used to form a composite nSES index presents challenges in summarizing overall associations with cancer. Given recent calls for standardized measures of neighborhood sociodemographic effects in cancer disparity research, the objective of this systematic review was to identify and compare existing nSES indices studied across the cancer continuum (incidence, screening, diagnosis, treatment, survival/mortality) and summarize associations by race/ethnicity and cancer site to inform future cancer disparity studies.Methods: Using PRISMA guidelines, peer‐reviewed articles published between 2010 and 2019 containing keywords related to nSES and cancer were identified in PubMed.Results: Twenty‐four nSES indices were identified from 75 studies. In general, findings indicated a significant association between nSES and cancer outcomes (n = 64/75 studies; 85.33%), with 42/64 (65.63%) adjusting for highly‐correlated individual SES factors (e.g., education). However, the direction of association differed by cancer site, race/ethnicity, and nSES index.Conclusions: This review highlights several methodologic and conceptual issues surrounding nSES measurement and potential associations with cancer disparities. Recommendations pertaining to the selection of nSES measures are provided, which may help inform disparity‐related disease processes and improve the identification of vulnerable populations in need of intervention.

Background: Mapping breast cancer survival can help cancer control programs prioritize efforts with limited resources. We used Bayesian spatial models to identify whether breast cancer survival among patients in New Jersey (NJ) varies spatially after adjusting for key individual (age, stage at diagnosis, molecular subtype, race/ethnicity, marital status, and insurance) and neighborhood measures of poverty and economic inequality [index of concentration at the extremes (ICE)]. Methods: Survival time was calculated for all NJ women diagnosed with invasive breast cancer between 2010 and 2014 and followed to December 31, 2015 (N = 27,078). Nonlinear geoadditive Bayesian models were used to estimate spatial variation in hazard rates and identify geographic areas of higher risk of death from breast cancer. Results: Significant geographic differences in breast cancer survival were found in NJ. The geographic variation of hazard rates statewide ranged from 0.71 to 1.42 after adjustment for age and stage, and were attenuated after adjustment for additional individual-level factors (0.87–1.15) and neighborhood measures, including poverty (0.9–1.11) and ICE (0.92–1.09). Neighborhood measures were independently associated with breast cancer survival, but we detected slightly stronger associations between breast cancer survival, and the ICE compared to poverty. Conclusions: The spatial models indicated breast cancer survival disparities are a result of combined individual-level and neighborhood socioeconomic factors. More research is needed to understand the moderating pathways in which neighborhood socioeconomic status influences breast cancer survival. Impact: More effective health interventions aimed at improving breast cancer survival could be developed if geographic variation were examined more routinely in the context of neighborhood socioeconomic inequalities in addition to individual characteristics.

Since 2021, the American Cancer Society has published its biennial report on the status of cancer disparities in the United States. In this 2025 report, the authors provide updated data on disparities in cancer occurrence and outcomes by sex, race, ethnicity, socioeconomic status (SES [educational attainment as a proxy]), and geographic location (including urbanicity of county of residence and congressional district), along with contributors to these disparities, including major cancer risk factors, screening, and select social determinants of health (SDOH) and health‐related social needs. The authors found substantial disparities across the cancer continuum, including risk factors, incidence, stage at diagnosis, receipt of care, survival, and mortality for many cancers and in evaluated SDOH by race and ethnicity, educational attainment, and geographic location. During 2019 through 2023, Black and American Indian/Alaska Native populations had the highest cancer mortality rates, both overall and for the leading causes of cancer death. Cancer mortality rates were also consistently higher among adults with lower SES. However, differences in cancer mortality were substantially larger by education than by race, indicating that SES plays a major role in driving racial disparities in cancer mortality. Overall cancer mortality rates were higher in Black adults than in White adults with the same education level by 7%–28% among males and by 2%–43% among females. Within each race, however, overall cancer mortality rates were higher in adults with ≤12 years of education than in those with ≥16 years of education by 143%–192% among males and by 71%–140% among females. Mortality from all cancers combined was 21% higher in nonmetropolitan than in large metropolitan counties, with the greatest differences for lung (45%) and cervical (36%) cancers and the smallest for prostate, female breast, and pancreatic cancers (7%–8%). By congressional district, the highest cancer mortality rates both overall and for lung, colorectal, and breast cancers were largely found in the South and East North‐Central division of the Midwest; however, for prostate cancer, there was no distinct geographic pattern. Sociodemographic groups that had higher cancer mortality generally had higher exposure to risk factors, lower health insurance coverage, and limited access to cancer prevention, early detection, and treatment compared with groups that had lower cancer mortality, largely reflecting fundamental disparities in SDOH. Mitigating cancer disparities in the United States requires intersectoral stakeholder engagement, targeted funding, effective policies at the federal, state, and local levels, and broad implementation of evidence‐based interventions, such as expanding health insurance coverage, including through strengthening Marketplaces and protecting and expanding access to Medicaid.

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Background: Residential histories linked to cancer registry data provide new opportunities to examine cancer outcomes by neighborhood socioeconomic status (SES). We examined differences in regional stage colon cancer survival estimates comparing models using a single neighborhood SES at diagnosis to models using neighborhood SES from residential histories. Methods: We linked regional stage colon cancers from the New Jersey State Cancer Registry diagnosed from 2006 to 2011 to LexisNexis administrative data to obtain residential histories. We defined neighborhood SES as census tract poverty based on location at diagnosis and across the follow-up period through 31 December 2016 based on residential histories (average, time-weighted average, time-varying). Using Cox proportional hazards regression, we estimated associations between colon cancer and census tract poverty measurements (continuous and categorical), adjusted for age, sex, race/ethnicity, regional substage, and mover status. Results: Sixty-five percent of the sample was nonmovers (one census tract); 35% (movers) changed tract at least once. Cases from tracts with >20% poverty changed residential tracts more often (42%) than cases from tracts with 20%) had a 30% higher risk of regional stage colon cancer death than cases in the lowest category (<5%) (95% confidence interval [CI] = 1.04, 1.63). Conclusion: Residential changes after regional stage colon cancer diagnosis may be associated with a higher risk of colon cancer death among cases in high-poverty areas. This has important implications for postdiagnostic access to care for treatment and follow-up surveillance. See video http://links.lww.com/EDE/B705.