BackgroundAs social media becomes increasingly popular online venues for engaging in communication about public health issues, it is important to understand how users promote knowledge and awareness about specific topics.ObjectiveThe aim of this study is to examine the frequency of discussion and differences by race and ethnicity of cancer-related topics among unique users via Twitter.MethodsTweets were collected from April 1, 2014 through January 21, 2015 using the Twitter public streaming Application Programming Interface (API) to collect 1% of public tweets. Twitter users were classified into racial and ethnic groups using a new text mining approach applied to English-only tweets. Each ethnic group was then analyzed for frequency in cancer-related terms within user timelines, investigated for changes over time and across groups, and measured for statistical significance.ResultsObservable usage patterns of the terms "cancer", "breast cancer", "prostate cancer", and "lung cancer" between Caucasian and African American groups were evident across the study period. We observed some variation in the frequency of term usage during months known to be labeled as cancer awareness months, particularly September, October, and November. Interestingly, we found that of the terms studied, "colorectal cancer" received the least Twitter attention.ConclusionsThe findings of the study provide evidence that social media can serve as a very powerful and important tool in implementing and disseminating critical prevention, screening, and treatment messages to the community in real-time. The study also introduced and tested a new methodology of identifying race and ethnicity among users of the social media. Study findings highlight the potential benefits of social media as a tool in reducing racial and ethnic disparities.

Exposures to carcinogens in hair products have been explored as breast cancer risk factors, yielding equivocal findings. We examined hair product use (hair dyes, chemical relaxers and cholesterol or placenta-containing conditioners) among African American (AA) and White women, and explored associations with breast cancer. Multivariable-adjusted models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to describe the associations of interest among 2280 cases (1508 AA and 772 White) and 2005 controls (1290 AA and 715 White). Among controls, hair dye use was more common among Whites than AAs (58 versus 30%), while relaxer (88 versus 5%) and deep conditioner use (59 versus 6%) was more common among AAs. Among AAs, use of dark hair dye shades was associated with increased breast cancer risk (OR = 1.51, 95% CI: 1.20-1.90) and use of dark shades (OR = 1.72, 95% CI: 1.30-2.26) and higher frequency of use (OR = 1.36, 95% CI: 1.01-1.84) were associated with ER+ disease. Among Whites, relaxer use (OR = 1.74, 95% CI: 1.11-2.74) and dual use of relaxers and hair dyes (OR = 2.40, 95% CI: 1.35-4.27) was associated with breast cancer; use of dark hair dyes was associated with increased ER+ disease (OR = 1.54, 95% CI: 1.01-2.33), and relaxer use was associated with increased ER- disease (OR = 2.56, 95% CI: 1.06-6.16). These novel findings provide support a relationship between the use of some hair products and breast cancer. Further examinations of hair products as important exposures contributing to breast cancer carcinogenesis are necessary.

Background-Few studies have empirically tested the association of allostatic load (AL) with breast cancer clinicopathology. The aim of this study was to examine the association of AL, measured using relevant biomarkers recorded in medical records before breast cancer diagnosis, with unfavorable tumor clinicopathologic features among Black women.Methods-In a sample of 409 Black women with non-metastatic breast cancer, who are enrolled in the Women's Circle of Health Follow-Up Study (WCHFS), we estimated pre-diagnostic AL using two measures: AL measure 1 (lipid profile-based -assessed by systolic and diastolic blood pressure [SBP, DBP], high-density lipoprotein, low-density lipoprotein, total cholesterol, triglycerides and glucose levels, waist circumference, and use of diabetes, hypertension, or hypercholesterolemia medication) and AL measure 2 (inflammatory index-based -assessed by SBP, DBP, glucose and albumin levels, estimated glomerular filtration rate, body mass index, waist circumference, and use of medications described above). We used Cohen's kappa statistic to assess agreement between the two AL measures and multivariable logistic models to assess the associations of interest.

BACKGROUND: Black women are more likely to have comorbidity at breast cancer diagnosis compared with White women, which may account for half of the Black-White survivor disparity. Comprehensive disease management requires a coordinated team of healthcare professionals including primary care practitioners, but few studies have examined shared care in the management of comorbidities during cancer care, especially among racial/ethnic minorities. OBJECTIVE: To examine whether the type of medical team composition is associated with optimal clinical care management of comorbidities. DESIGN: We used the Women's Circle of Health Follow-up Study, a population-based cohort of Black women diagnosed with breast cancer. The likelihood of receiving optimal comorbidity management after breast cancer diagnosis was compared by type of medical team composition (shared care versus cancer specialists only) using binomial regression. PARTICIPANTS: Black women with a co-diagnosis of diabetes and/or hypertension at breast cancer diagnosis between 2012 and 2016 (N = 274). MAIN MEASURES: Outcome-optimal clinical care management of diabetes (i.e., A1C test, LDL-C test, and medical attention for nephropathy) and hypertension (i.e., lipid screening and prescription for hypertension medication). Main predictor-shared care, whether the patient received care from both a cancer specialist and a primary care provider and/or a medical specialist within the 12 months following a breast cancer diagnosis. KEY RESULTS: Primary care providers were the main providers involved in managing comorbidities and 90% of patients received shared care during breast cancer care. Only 54% had optimal comorbidity management. Patients with shared care were five times (aRR: 4.62; 95% CI: 1.66, 12.84) more likely to have optimal comorbidity management compared with patients who only saw cancer specialists. CONCLUSIONS: Suboptimal management of comorbidities during breast cancer care exists for Black women. However, our findings suggest that shared care is more beneficial at achieving optimal clinical care management for diabetes and hypertension than cancer specialists alone.

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.