Stage at diagnosis and cancer-specific survival for stomach, lung, colorectal, and bladder cancers among Armenians in California.

Objective: To observe stage at diagnosis and cancer-specific survival for common cancers among Armenians in California.

Methods: We used the Armenian Surname List and birthplace information in the California Cancer Registry to identify Armenians with stomach, lung, colorectal, and bladder cancers diagnosed during 1988-2019. We used multivariable logistic regression models to calculate odds of late-stage diagnoses among Armenian and non-Armenian, non-Hispanic White patients and examine the association of sociodemographic factors with late-stage diagnoses among the Armenian patient population.

Debiasing large language models: research opportunities.

Large language models (LLMs) are powerful decision-making tools widely adopted in healthcare, finance, and transportation. Embracing the opportunities and innovations of LLMs is inevitable. However, LLMs inherit stereotypes, misrepresentations, discrimination, and societies' biases from various sources-including training data, algorithm design, and user interactions-resulting in concerns about equality, diversity, and fairness.

Sun protection behaviors, healthcare access, and smoking among melanoma survivors.

Purpose: Based on current clinical practice guidelines, melanoma survivors should be advised on the need for sun protection and regular healthcare, as well as smoking cessation, but differences from adults without cancer history are unclear.

Methods: We pooled data from the National Health and Nutrition Examination Survey (2003-2006, 2009-2018), matching 249 melanoma survivors with 498 adults without a cancer history. Adjusted prevalence odds ratios (aPOR) and 95% confidence intervals (CI) were calculated.

Breast cancer screening needs assessment in 19 Northern California counties: geography, poverty, and racial/ethnic identity composition.

Purpose: To describe the area-level rate of breast cancers, the percentage of early-stage diagnoses (stage I-IIa), and associations between area-level measures of poverty, racial/ethnic composition, primary care shortage, and urban/rural/frontier status for the UC Davis Comprehensive Cancer Center (UCDCCC) catchment area.

Methods: Using data from the SEER Cancer Registry of Greater California (2014-2018) and the California Department of Health Care Access and Information Medical Service Study Area, we conducted an ecological study in the UCDCCC catchment area to identify geographies that need screening interventions and their demographic characteristics.

Results: The higher the percentage of the population identifying as Hispanic/Latino/Latinx, and the higher the percentage of the population below the 100% poverty level, the lower the odds of being diagnosed at an early-stage (OR = 0.

Syndemic geographic patterns of cancer risk in a health-deprived area of England.

Objectives: This study aims to analyse the geographical co-occurrence of cancers and their individual and shared risk factors in a highly deprived area of the North West of England to aid the identification of potential interventions.

Study Design: An ecological study design was employed and applied at postcode sector level in the Morecambe Bay region.

Methods: A novel spatial joint modelling framework designed to account for large frequencies of left-censored cancer data was employed.