Determinants of survival in women diagnosed with breast cancer between 2008 and 2017: An analysis of a cohort using data from four Population-Based Cancer Registries of Colombia
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Breast cancer survival in Colombia is significantly impacted by socioeconomic factors and healthcare access. Vulnerable populations, including older women and those with lower socioeconomic status, face poorer outcomes, highlighting the need for targeted interventions.
Area Of Science
- Oncology
- Public Health
- Epidemiology
Background
- Breast cancer (BC) is a leading cause of cancer mortality in women globally, particularly in low- and middle-income countries.
- Population-Based Cancer Registries (PBCRs) are vital for monitoring cancer trends and informing control strategies.
- Colombia lacks national analyses of BC survival, necessitating population-based data evaluation.
Purpose Of The Study
- To estimate the 5-year overall survival for women diagnosed with invasive breast cancer in Colombia.
- To identify determinants of breast cancer survival using population-based data from four Colombian PBCRs.
Main Methods
- A cohort study was conducted using data from 8020 women diagnosed with invasive BC between 2008 and 2017 from four Colombian PBCRs.
- Follow-up was performed for up to 5 years post-diagnosis or until death (all-cause).
- Kaplan-Meier survival analysis and Cox proportional hazards models were used to estimate survival and identify risk factors.
Main Results
- The overall observed survival rate was 72.5%.
- Factors associated with significantly lower 5-year survival included older age (≥70 years), middle socioeconomic stratum (SES), subsidized Health Insurance Regime (HIR) affiliation, and advanced diagnosis stage (III-IV).
- Adjusted Hazard Ratios (aHR) indicated increased mortality risk for these groups compared to younger women, higher SES, contributory HIR, and early-stage diagnoses.
Conclusions
- Social disparities significantly influence breast cancer survival in Colombia, likely due to disparities in healthcare access.
- Strengthening screening and diagnostic services is crucial, particularly for vulnerable populations, to improve breast cancer outcomes.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
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...
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...
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Survival Times Are Positively Skewed
Survival times often exhibit positive skewness, unlike the normal distribution assumed...
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...