Guideline-concordant breast cancer care by patient race and ethnicity accounting for individual-, facility- and area-level characteristics: a SEER-Medicare study.

PURPOSE: To examine racial-ethnic variation in adherence to established quality metrics (NCCN guidelines and ASCO quality metrics) for breast cancer, accounting for individual-, facility-, and area-level factors. METHODS: Data from women diagnosed with invasive breast cancer at 66+ years of age from 2000 to 2017 were examined using SEER-Medicare. Associations between race and ethnicity and guideline-concordant diagnostics, locoregional treatment, systemic therapy, documented stage, and oncologist encounters were estimated using multilevel logistic regression models to account for clustering within facilities or counties. RESULTS: Black and American Indian/Alaska Native (AIAN) women had consistently lower odds of guideline-recommended care than non-Hispanic White (NHW) women (Diagnostic workup: ORBlack 0.83 (0.79-0.88), ORAIAN 0.66 (0.54-0.81); known stage: ORBlack 0.87 (0.80-0.94), ORAIAN 0.63 (0.47-0.85); seeing an oncologist: ORBlack 0.75 (0.71-0.79), ORAIAN 0.60 (0.47-0.72); locoregional treatment: ORBlack 0.80 (0.76-0.84), ORAIAN 0.84 (0.68-1.02); systemic therapies: ORBlack 0.90 (0.83-0.98), ORAIAN 0.66 (0.48-0.91)). Commission on Cancer accreditation and facility volume were significantly associated with higher odds of guideline-concordant diagnostics, stage, oncologist visits, and systemic therapy. Black residential segregation was associated with significantly lower odds of guideline-concordant locoregional treatment and systemic therapy. Rurality and area SES were associated with significantly lower odds of guideline-concordant diagnostics and oncologist visits. CONCLUSIONS: This is the first study to examine guideline-concordance across the continuum of breast cancer care from diagnosis to treatment initiation. Disparities were present from the diagnostic phase and persisted throughout the clinical course. Facility and area characteristics may facilitate or pose barriers to guideline-adherent treatment and warrant future investigation as mediators of racial-ethnic disparities in breast cancer care.

A test of the predictive validity of relative versus absolute income for self-reported health and well-being in the United States.

BACKGROUND: A classic debate concerns whether absolute or relative income is more salient. Absolute values resources as constant across time and place while relative contextualizes one's hierarchical location in the distribution of a time and place. OBJECTIVE: This study investigates specifically whether absolute income or relative income matters more for health and well-being. METHODS: We exploit within-person, within-age, and within-time variation with higher-quality income measures and multiple health and well-being outcomes in the United States. Using the Panel Study of Income Dynamics and the Cross-National Equivalent File, we estimate three-way fixed effects models of self-rated health, poor health, psychological distress, and life satisfaction. RESULTS: For all four outcomes, relative income has much larger standardized coefficients than absolute income. Robustly, the confidence intervals for relative income do not overlap with zero. By contrast, absolute income mostly has confidence intervals that overlap with zero, and its coefficient is occasionally signed in the wrong direction. A variety of robustness checks support these results. CONCLUSIONS: Relative income has far greater predictive validity than absolute income for self-reported health and well-being. CONTRIBUTION: Compared to earlier studies, this study provides a more rigorous comparison and test of the predictive validity of absolute and relative income that is uniquely conducted with data on the United States. This informs debates on income measurement, the sources of health and well-being, and inequalities generally. Plausibly, these results can guide any analysis that includes income in models.

Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic.

PURPOSE: Methods for assessing the structural mechanisms of health inequity are not well established. This study applies a phased approach to modeling racial, occupational, and rural disparities on the county level. METHODS: Rural counties with disparately high rates of COVID-19 incidence or mortality were randomly paired with in-state control counties with the same rural-urban continuum code. Analysis was restricted to the first six months of the pandemic to represent the baseline structural reserves for each county and reduce biases related to the disruption of these reserves over time. Conditional logistic regression was applied in two phases-first, to examine the demographic distribution of disparities and then, to examine the relationships between these disparities and county-level social and structural reserves. RESULTS: In over 200 rural county pairs (205 for incidence, 209 for mortality), disparities were associated with structural variables representing economic factors, healthcare infrastructure, and local industry. Modeling results were sensitive to assumptions about the relationships between race and other social and structural variables measured at the county level, particularly in models intended to reflect effect modification or mediation. CONCLUSIONS: Multivariable modeling of health disparities should reflect the social and structural mechanisms of inequity and anticipate interventions that can advance equity.

Patterns of Evidence-Based Care for the Diagnosis, Staging, and First-line Treatment of Breast Cancer by Race-Ethnicity: A SEER-Medicare Study.

BACKGROUND: Racial and ethnic disparities in guideline-recommended breast cancer treatment are well documented, however studies including diagnostic and staging procedures necessary to determine treatment indications are lacking. The purpose of this study was to characterize patterns in delivery of evidence-based services for the diagnosis, clinical workup, and first-line treatment of breast cancer by race-ethnicity. METHODS: SEER-Medicare data were used to identify women diagnosed with invasive breast cancer between 2000 and 2017 at age 66 or older (n = 2,15,605). Evidence-based services included diagnostic procedures (diagnostic mammography and breast biopsy), clinical workup (stage and grade determination, lymph node biopsy, and HR and HER2 status determination), and treatment initiation (surgery, radiation, chemotherapy, hormone therapy, and HER2-targeted therapy). Poisson regression was used to estimate rate ratios (RR) and 95% confidence intervals (CI) for each service. RESULTS: Black and American Indian/Alaska Native (AIAN) women had significantly lower rates of evidence-based care across the continuum from diagnostics through first-line treatment compared to non-Hispanic White (NHW) women. AIAN women had the lowest rates of HER2-targeted therapy and hormone therapy initiation. While Black women also had lower initiation of HER2-targeted therapy than NHW, differences in hormone therapy were not observed. CONCLUSIONS: Our findings suggest patterns along the continuum of care from diagnostic procedures to treatment initiation may differ across race-ethnicity groups. IMPACT: Efforts to improve delivery of guideline-concordant treatment and mitigate racial-ethnic disparities in healthcare and survival should include procedures performed as part of the diagnosis, clinical workup, and staging processes.

Income Inequality and Population Health: A Global Gradient?

Cross-national empirical research about the link between income inequality and population health produces conflicting conclusions. We address these mixed findings by examining the degree to which the income inequality and health relationship varies with economic development. We estimate fixed-effects models with different measures of income inequality and population health. Results suggest that development moderates the association between inequality and two measures of population health. Our findings produce two generalizations. First, we observe a global gradient in the relationship between income inequality and population health. Second, our results are consistent with income inequality as a proximate or conditional cause of lower population health. Income inequality has a 139.7% to 374.3% more harmful effect on health in poorer than richer countries and a significantly harmful effect in 2.1% to 53.3% of countries in our sample and 6.6% to 67.6% of the world's population but no significantly harmful effect in richer countries.

Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention.

Web and mobile (mHealth) interventions have promise for improving health outcomes, but engagement and attrition may be reducing effect sizes. Because social networks can improve engagement, which is a key mechanism of action, understanding the structure and potential impact of social networks could be key to improving mHealth effects. This study (a) evaluates social network characteristics of four distinct communication channels (discussion board, chat, e-mail, and blog) in a large social networking intervention, (b) predicts membership in online communities, and (c) evaluates whether community membership impacts engagement. Participants were 299 cancer survivors with significant distress using the 12-week health-space.net intervention. Social networking attributes (e.g., density and clustering) were identified separately for each type of network communication (i.e., discussion board, blog, web mail, and chat). Each channel demonstrated high levels of clustering, and being a community member in one communication channel was associated with being in the same community in each of the other channels (φ = 0.56-0.89, ps < 0.05). Predictors of community membership differed across communication channels, suggesting that each channel reached distinct types of users. Finally, membership in a discussion board, chat, or blog community was strongly associated with time spent engaging with coping skills exercises (Ds = 1.08-1.84, ps < 0.001) and total time of intervention (Ds = 1.13-1.80, ps < 0.001). mHealth interventions that offer multiple channels for communication allow participants to expand the number of individuals with whom they are communicating, create opportunities for communicating with different individuals in distinct channels, and likely enhance overall engagement.