Mindy C. DeRouen

Background: Biomarkers in the hormonal and metabolic pathways linking obesity and mortality can influence survival after breast cancer diagnosis. Methods: We prospectively examined associations with mortality for prediagnostic plasma levels of circulating 27-hydroxycholesterol (27HC), estrone, testosterone, and sex hormone–binding globulin (SHBG) measured on average 6.7 years before breast cancer diagnosis in 1,009 African American, Japanese American, Latino, Native Hawaiian, and White postmenopausal females not using menopausal hormone therapy at blood draw from the Multiethnic Cohort Study (1993–2017). Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using multivariable Cox proportional hazards regression, adjusting for selected factors related to demographics, prognosis, lifestyle, and medical history. Results: Over an average 7.8 follow-up years (standard deviation = 4.2), 300 total deaths occurred, including 83 breast cancer–specific deaths. Inverse associations were found for 27HC (highest vs. lowest tertile: HR, 0.73; 95% CI, 0.54–0.98) with all-cause but not breast cancer–specific mortality and for estrone (highest vs. lowest tertile: HR, 0.37; 95% CI, 0.20–0.70) with breast cancer–specific but not all-cause mortality. SHBG was positively associated with all-cause (highest vs. lowest tertile: HR, 1.76; 95% CI, 1.28–2.41) but not breast cancer–specific mortality. No associations were found for testosterone. There was no statistically significant heterogeneity in associations by body mass index, estrogen receptor status, cancer stage, or race and ethnicity. Conclusions: This seminal study of racially and ethnically diverse female breast cancer survivors found associations for prediagnostic 27HC, estrone, and SHBG with mortality. Impact: Future research studies should prioritize larger breast cancer cohorts to confirm our findings and evaluate associations across important subgroups.

Objective: We examined associations between multilevel sociodemographic factors and extent of disease at presentation and time to treatment in patients with differentiated thyroid cancer (DTC). Study Design: Population‐based study. Setting: California Cancer Registry (CCR). Methods: All adults diagnosed with DTC in California from January 1, 1988 to December 31, 2020 were included. Main independent variables were neighborhood socioeconomic status (nSES) and race/ethnicity. Covariates included individual sex, marital status, insurance type, and year and age at diagnosis. Primary outcomes were tumor histology, size, and stage at diagnosis. Secondary outcome was time from diagnosis to treatment. Results: Among 88,894 individuals diagnosed with DTC, 77% were female, more were residents of higher SES neighborhoods (24% Q5 vs 13% Q1), and nearly half (48%) were non‐Hispanic (NH) White, followed by 27% Hispanic and 7% of another NH Asian American ethnicity. In multivariable models, the odds of presenting with distant metastases were higher with lower nSES (OR Q1 vs Q5 = 1.33; 95% CI = 1.17, 1.50), male sex (OR female vs male = 0.43; 95% CI = 0.40, 0.47), and Black (OR = 1.24; 95% CI = 1.04, 1.49), Hispanic (OR = 2.02; 95% CI = 1.84, 2.21), Chinese American (OR = 1.80; 95% CI = 1.50, 2.16), Filipino American (OR = 2.07; 95% CI = 1.81, 2.36), or another Asian American (OR = 1.78; 95% CI = 1.56, 2.04) race/ethnicity. Lower nSES was associated with longer time from diagnosis to treatment (OR = 1.24; 95% CI 1.12, 1.38). Conclusions: People of lower nSES, male sex, and certain racial/ethnic groups were diagnosed with more advanced disease and had longer times to receipt of definitive treatment. Multilevel sociodemographic factors impact DTC presentation and treatment access.

Places have a significant impact on health. Thus, examining place-based structural and social determinants can help inform effective public health interventions. HealthAtlas.ucsf.edu provides a single online platform to explore multiple domains of place-based data. The initial California-only version of Health Atlas was launched in April 2020, and a national version was launched in October 2024. An artificial intelligence–assisted search function was integrated in January 2026. Health Atlas includes data on over 200 variables across 5 topical domains: demographics, socioeconomic, neighborhood, environment, and health and health care. The data were either (1) obtained from the American Community Survey, CDC PLACES, and other public sources or (2) obtained through collaboration with research partners. Users can visualize and aggregate data for 7 geographic levels: census tract, zip code tabulation area, county, congressional district, core-based statistical area, public use microdata area, and state. Most variables are available across all 50 states in the United States, the District of Columbia, and Puerto Rico. Users can explore Health Atlas to better understand the relationship between selected variables via maps, histograms, scatterplots, and summary plots. Users can select custom areas to suit specific needs. Customizable datasets can be downloaded for further use. Health Atlas provides freely available, user-friendly data and tools to support researchers, community organizations, government entities, and other public health professionals. We envision that Health Atlas will contribute to evidence-based, community-based initiatives; impactful health equity research; and effective public health programs.

Characteristics of people diagnosed with invasive tumors in California in 2001 to 2019, 2020, 2021, and 2022.

Ratio of observed / expected invasive tumors in the Greater Bay Area of California in 2022 overall and for 12 common cancers, by sociodemographic characteristics, stage at diagnosis, and rurality.

Ratio of observed / expected invasive tumors in the Los Angeles Cancer Surveillance Program in 2022, overall and for 12 common cancers, by sociodemographic characteristics and stage at diagnosis.

Details for the ICD-O-3 site and histology codes used to define the cancer types and the SEER*Stat variables used for analysis.

Background:The reduction in cancer cases in 2020 has been attributed to missed diagnoses during the pandemic, yet no rebound occurred in 2021. We examined whether cancer rebounded in California in 2022.Methods:Data on invasive tumors in California for 2001 to 2022 were obtained through the NCI Surveillance, Epidemiology, and End Results program. Expected cases for 2022 were calculated using joinpoint regression models based on trends from 2001 to 2021 (omitting 2020). We calculated the ratio of observed to expected (O/E) cases in 2022 and 95% confidence intervals (CI).Results:There was no overall difference in observed versus expected invasive tumors in California in 2022. However, lung cancer was 29% lower than expected in rural areas (O/E: 0.71; 95% CI, 0.39–0.95), uterine cancer was lower than expected in Los Angeles (O/E: 0.91; 95% CI, 0.86–0.96), and breast cancer was lower than expected in Greater California (O/E: 0.95; 95% CI, 0.90–0.98). Groups with more tumors observed than expected included unstaged colorectal cancer in Greater California (O/E: 1.54) and Los Angeles (O/E: 1.26), colorectal cancer among males (O/E: 1.12) and Asian Americans (O/E: 1.16) in the Greater Bay Area, localized melanoma in Greater California (O/E: 1.24), and prostate cancer among Hispanic males in Los Angeles (O/E: 1.24).Conclusions:Overall, observed invasive tumors did not differ from expected in California in 2022, but there was variation across demographic, regional, and clinical factors. The reduction in cancer cases in 2020 likely reflects multiple factors, including missed diagnoses and death due to competing causes.Impact:Continued close monitoring of cancer diagnoses is needed to inform cancer control strategies.

Ratio of observed/expected invasive tumors in the Cancer Registry of Greater California in 2022, overall and for 12 common cancers, by sociodemographic characteristics, stage, and rurality.