The Implications of Racialized Economic Segregation and Allostatic Load on Mortality in Patients with Breast Cancer.

BACKGROUND: The objective of this study was to examine the association between racialized economic segregation, allostatic load (AL), and all-cause mortality in patients with breast cancer. PATIENTS AND METHODS: Women aged 18+ years with stage I-III breast cancer diagnosed between 01/01/2012 and 31/12/2020 were identified in the Ohio State University cancer registry. Racialized economic segregation was measured at the census tract level using the index of concentration at the extremes (ICE). AL was calculated with biomarkers from the cardiac, metabolic, immune, and renal systems. High AL was defined as AL greater than the median. Univariable and multivariable regression analyses using restricted cubic splines examined the association between racialized economic segregation, AL, and all-cause mortality. RESULTS: Among 4296 patients, patients residing in neighborhoods with the highest racialized economic segregation (Q1 versus Q4) were more likely to be Black (25% versus 2.1%, p < 0.001) and have triple-negative breast cancer (18.2% versus 11.6%, p < 0.001). High versus low racialized economic segregation was associated with high AL [adjusted odds ratio (aOR) 1.40, 95% confidence interval (CI) 1.21-1.61] and worse all-cause mortality [adjusted hazard ratio (aHR) 1.41, 95% CI 1.08-1.83]. In dose-response analyses, patients in lower segregated neighborhoods (relative to the 95th percentile) had lower odds of high AL, whereas patients in more segregated neighborhoods had a non-linear increase in the odds of high AL. DISCUSSION: Racialized economic segregation is associated with high AL and a greater risk of all-cause mortality in patients with breast cancer. Additional studies are needed to elucidate the causal pathways and mechanisms linking AL, neighborhood factors, and patient outcomes.

Marijuana and Cannabidiol Use Prevalence and Symptom Management Among Patients with Cancer.

Symptoms such as pain, nausea, and anxiety are common in individuals with cancer. Treatment of these issues is often challenging. Cannabis products may be helpful in reducing the severity of these symptoms. While some studies include data on the prevalence of cannabis use among patients with cancer, detailed data remain limited, and none have reported the prevalence of cannabidiol (CBD) use in this population. Adult patients with cancer attending eight clinics at a large, NCI-designated Comprehensive Cancer Center completed a detailed, cannabis-focused questionnaire between 2021 and 2022. Eligible participants were diagnosed with invasive cancer and treated in the past 12 months. Summary statistics were calculated to describe the sample regarding cannabis use. Approximately 15% (n = 142) of consented patients (n = 934) reported current cannabis use (defined as use within the past 12 months). Among which, 75% reported cannabis use in the past week. Among current cannabis users, 39% (n = 56; 6% overall) used CBD products. Current users reported using cannabis a median of 4.5 (interquartile range: 0.6­7.0) days/week, 2.0 (1.0­3.0) times per use/day, and for 3 years (0.8­30.0). Use patterns varied by route of administration. Patients reported moderate to high relief of symptoms with cannabis use. This study is the most detailed to date in terms of cannabis measurement and provides information about the current state of cannabis use in active cancer. Future studies should include complete assessments of cannabis product use, multiple recruitment sites, and diverse patient populations. SIGNIFICANCE: Clinicians should be aware that patients are using cannabis products and perceive symptom relief with its use.

Spatial predictive properties of built environment characteristics assessed by drop-and-spin virtual neighborhood auditing.

BACKGROUND: Virtual neighborhood audits have been used to visually assess characteristics of the built environment for health research. Few studies have investigated spatial predictive properties of audit item responses patterns, which are important for sampling efficiency and audit item selection. We investigated the spatial properties, with a focus on predictive accuracy, of 31 individual audit items related to built environment in a major Metropolitan region of the Northeast United States. METHODS: Approximately 8000 Google Street View (GSV) scenes were assessed using the CANVAS virtual audit tool. Eleven trained raters audited the 360° view of each GSV scene for 10 sidewalk-, 10 intersection-, and 11 neighborhood physical disorder-related characteristics. Nested semivariograms and regression Kriging were used to investigate the presence and influence of both large- and small-spatial scale relationships as well as the role of rater variability on audit item spatial properties (measurement error, spatial autocorrelation, prediction accuracy). Receiver Operator Curve (ROC) Area Under the Curve (AUC) based on cross-validated spatial models summarized overall predictive accuracy. Correlations between predicted audit item responses and select demographic, economic, and housing characteristics were investigated. RESULTS: Prediction accuracy was better within spatial models of all items accounting for both small-scale and large- spatial scale variation (vs large-scale only), and further improved with additional adjustment for rater in a majority of modeled items. Spatial predictive accuracy was considered 'Excellent' (0.8 ≤ ROC AUC < 0.9) for full models of all but four items. Predictive accuracy was highest and improved the most with rater adjustment for neighborhood physical disorder-related items. The largest gains in predictive accuracy comparing large- + small-scale to large-scale only models were among intersection- and sidewalk-items. Predicted responses to neighborhood physical disorder-related items correlated strongly with one another and were also strongly correlated with racial-ethnic composition, socioeconomic indicators, and residential mobility. CONCLUSIONS: Audits of sidewalk and intersection characteristics exhibit pronounced variability, requiring more spatially dense samples than neighborhood physical disorder audits do for equivalent accuracy. Incorporating rater effects into spatial models improves predictive accuracy especially among neighborhood physical disorder-related items.

Latino residential segregation and self-rated health among Latinos: Washington State Behavioral Risk Factor Surveillance System, 2012-2014.

The relationship between Latino residential segregation and self-rated health (SRH) is unclear, but might be partially affected by social capital. We investigated the association between Latino residential segregation and SRH while also examining the roles of various social capital measures. Washington State Behavioral Risk Factor Surveillance System (2012-2014) and U.S. Census data were linked by zip code and zip code tabulation area. Multilevel logistic regression models were used to estimate odds of good or better SRH by Latino residential segregation, measured by the Gini coefficient, and controlling for sociodemographic, acculturation and social capital measures of neighborhood ties, collective socialization of children, and social control. The Latino residential segregation - SRH relationship was convex, or 'U'-shaped, such that increases in segregation among Latinos residing in lower segregation areas was associated with lower SRH while increases in segregation among Latinos residing in higher segregation areas was associated with higher SRH. The social capital measures were independently associated with SRH but had little effect on the relationship between Latino residential segregation and SRH. A convex relationship between Latino residential segregation and SRH could explain mixed findings of previous studies. Although important for SRH, social capital measures of neighborhood ties, collective socialization of children, and social control might not account for the relationship between Latino residential segregation and SRH.

Primary care physician supply, insurance type, and late-stage cancer diagnosis.

BACKGROUND: Understanding the joint effects of insurance type and primary care physician density on stage at diagnosis is essential to elucidating the healthcare access and late-stage cancer relationship. PURPOSE: To determine if the relationship between primary care physician density and odds of late-stage cancer are modified by insurance type at diagnosis. METHODS: Case patients were Ohio adults diagnosed between 1996 and 2008 with cancer of one of the following sites: female breast, cervix, colon/rectum, lung/bronchus, melanoma of the skin, oral cavity and pharynx, or prostate (N=376,425). County-level physician density was obtained from the Ohio Department of Health. Multilevel logistic regression models estimated odds ratios of late-stage cancer diagnosis associated with increases in primary care physician density by insurance type. Analyses were conducted in 2014. RESULTS: Decreases in late-stage diagnosis of cancers of the breast, prostate, melanoma of the skin, oral cavity and pharynx, or lung/bronchus associated with increases in primary care physician density were strongest among those with private insurance, whereas those with Medicare (prostate, oral cavity and pharynx, lung/bronchus), Medicaid (lung/bronchus), uninsured (prostate), and other/unknown (prostate, oral cavity and pharynx, lung/bronchus) did not benefit as greatly, or experienced significant increases in late-stage cancer diagnosis (other/unknown [female breast], Medicaid [melanoma of the skin], and uninsured [colon/rectum]). CONCLUSIONS: As primary care physician density increases, those with private insurance consistently benefit the most in terms of late-stage cancer diagnosis, whereas those with several other insurance types experience flatter decreases or significantly higher odds of late-stage cancer diagnosis.

Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data.

BACKGROUND: Glioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveillance Epidemiology and End Results (SEER) data. METHODS: Cases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence. RESULTS: Those residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit. CONCLUSION: Demographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship.