Related Experiment Video

Updated: Jan 6, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Racial and Ethnic Disparities in Cancer Mortality and Mediating Effects of Sociodemographic, Clinicopathologic, and

Olajumoke A Olateju¹, Hassan E Arekemase^2,3, Hemalkumar B Mehta⁴

¹Department of Pharmaceutical Health Outcomes and Policy, University of Houston College of Pharmacy, Houston, TX, USA.

Journal of Racial and Ethnic Health Disparities

|September 25, 2025

Abstract

Background:

To evaluate the associations between race/ethnicity and cancer-specific and all-cause mortality and to explore potential mediating factors.

Methods:

We identified patients diagnosed with lung (22,866), colorectal (13,382), melanoma (6894), prostate (22,922), and breast (133,366) cancers between 2010 and 2019 from the Texas Cancer Registry. Differences in mortality risk among non-Hispanic Blacks, Asian American/Native Hawaiian/Pacific Islanders (AANHPI), and Hispanics compared to non-Hispanic Whites were estimated using Cox proportional hazard and restricted mean survival time analyses. Mediation analysis was conducted to quantify the contributions of socioeconomic, clinicopathologic, and treatment-related factors to observed mortality differences.

Results:

Racial/ethnic disparities in survival were observed across all cancers, with the smallest disparity for lung cancer and the largest for melanoma and breast cancer. In age-and-sex-adjusted models, Blacks had higher mortality risks for all cancers observed. Hispanics showed higher mortality risks for non-small cell lung cancer, melanoma, and breast cancer, while AANHPIs had lower mortality risks for lung and breast cancer. After adjusting for demographic, clinical, and treatment factors, Blacks still had significantly higher mortality risks for melanoma (HR = 1.63, 95% CI = 1.49-1.70) and breast cancer (HR = 1.19, 95% CI = 1.09-1.30). Socioeconomic and clinicopathologic factors were major mediators of racial/ethnic disparities, accounting for 56.71% of the difference in melanoma mortality and 54.10% in breast cancer mortality between Blacks and Whites.

Conclusions:

Racial/ethnic disparities in cancer outcomes persist, particularly for melanoma and breast cancer among Black patients. Interventions promoting equity in cancer outcomes should be tailored by cancer type and take a multidisciplinary approach, integrating both social and biological factors to address disparities effectively.

Keywords:

Cancer Multiple mediation analysis Racial disparity Survival analysis

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Cancer Prevention

Cancer Prevention

Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Stereotypes, Prejudice, and Discrimination

Stereotypes, Prejudice, and Discrimination

Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...

Articles linked to this work by shared authors, journal, and citation graph.

Pharmacokinetics of Efavirenz in Pregnancy and Postpartum with and Without First-line Tuberculosis Drugs: Results from IMPAACT P1026s.

Clinical pharmacokinetics·2026

Integrated proteomics and metabolomics for predicting cardiometabolic risk in pregnant women living with HIV: current evidence and future directions.

Expert review of molecular diagnostics·2026

Obstetric pharmacoeconomics.

Expert review of pharmacoeconomics & outcomes research·2026

Trends in Why Americans Delayed Dental Care From Pre-Pandemic to the COVID-19 Era: Implications for Aging and Oral Public Health.

medRxiv : the preprint server for health sciences·2026

IMPAACT 2041: Designing the Study of Glecaprevir/Pibrentasvir Treatment During Pregnancy.

Open forum infectious diseases·2026

Dementias in Older Women With Early-Stage HER2-Negative Breast Cancer Treated With Anthracycline Plus Taxane Versus Taxane-Based Chemotherapy Regimens.

Pharmacoepidemiology and drug safety·2026

See all related articles