Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Obstetrics and Gynecology
Ethnic Disparities In Cervical Cancer Survival Among Texas Women, Ann L. Coker, Christopher P. Desimone, Katherine S. Eggleston, Arica L. White, Melanie Williams
Ethnic Disparities In Cervical Cancer Survival Among Texas Women, Ann L. Coker, Christopher P. Desimone, Katherine S. Eggleston, Arica L. White, Melanie Williams
CRVAW Faculty Journal Articles
Objective: The aim of this work was to determine whether minority women are more likely to die of cervical cancer. A population-based cohort study was performed using Texas Cancer Registry (TCR) data from 1998 to 2002.
Methods: A total of 5,166 women with cervical cancer were identified during 1998–2002 through the TCR. Measures of socioeconomic status (SES) and urbanization were created using census block group-level data. Multilevel logistic regression was used to calculate the odds of dying from cervical cancer by race, and Cox proportional hazards modeling was used for cervical cancer-specific survival analysis.
Results: After adjusting for age, SES, …
Cervical Cancer Survival By Socioeconomic Status, Race/Ethnicity, And Place Of Residence In Texas, 1995–2001, Katherine S. Eggleston, Ann L. Coker, Melanie Williams, Guillermo Tortolero-Luna, Jeanne B. Martin, Susan R. Tortolero
Cervical Cancer Survival By Socioeconomic Status, Race/Ethnicity, And Place Of Residence In Texas, 1995–2001, Katherine S. Eggleston, Ann L. Coker, Melanie Williams, Guillermo Tortolero-Luna, Jeanne B. Martin, Susan R. Tortolero
CRVAW Faculty Journal Articles
Objective: The current study explored whether socioeconomic status (SES), race/ethnicity, and rural residence may be linked to poorer cervical cancer survival by stage at diagnosis.
Methods: Data from 7,237 cervical cancer cases reported to the Texas Cancer Registry from 1995–2001 were used to address the association by stage at diagnosis and cause of death. Zip code-level census data were used to classify residence and to develop a composite variable for SES. Multilevel Cox proportional hazards modeling was used to estimate hazard ratios
(HR) and 95% confidence intervals (CI).
Results: Late stage at diagnosis was a strong predictor …