Related Experiment Video
Updated: Jul 17, 2025
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
300
Urban-rural differences in cancer mortality: Operationalizing rurality
Elizabeth S Davis1, Jeffrey A Franks2, Smita Bhatia3,4
1Department of Surgery, Boston University/Boston Medical Center, Boston, Massachusetts.
Summary
Data-driven rurality definitions reveal smaller urban-rural cancer mortality gaps. Continuous rurality measures better capture cancer mortality risks, highlighting the need for nuanced approaches in research and policy.
Area of Science:
- Epidemiology
- Public Health
- Rural Health Disparities
Background:
- Urban-rural disparities in cancer mortality are significant.
- Existing definitions of rurality may influence observed differences.
- Accurate classification of rurality is crucial for public health interventions.
Purpose of the Study:
- To compare urban-rural cancer mortality differences using various rurality definitions.
- To assess the impact of established vs. data-driven cut-points for rurality.
- To evaluate cancer mortality risk across a continuum of rurality.
Main Methods:
- Utilized Surveillance, Epidemiology, and End Results (SEER) data (2000-2016).
- Analyzed incident adult screening-related cancers across multiple cohorts.
- Defined urban-rural status using established codes, data-driven cut-points, and continuous measures.
Main Results:
- Data-driven cut-points classified more individuals as rural, reducing estimated urban-rural mortality differences (7%-14%).
- Cancer mortality risk increased with continuous rurality measures (4%-67%).
- Different rurality definitions yielded varying estimates of urban-rural disparities.
Conclusions:
- Binary cut-points for rurality introduce variability in mortality difference estimates.
- Continuous rurality measures offer a more nuanced understanding of cancer mortality risk.
- Findings support using continuous rurality measures for future research and policy.
Related Concept Videos
01:21Cancer Survival Analysis
381
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...
381
01:20Comparing the Survival Analysis of Two or More Groups
222
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...
222
01:25Statistical Methods for Analyzing Epidemiological Data
408
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:
408
01:29Bias in Epidemiological Studies
349
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:
349
01:12Relative Risk
208
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
208
01:03Rous Sarcoma Virus (RSV) and Cancer
5.1K
Rous Sarcoma virus or RSV was discovered by F. Peyton Rous in the year 1911 as a filterable transmissible agent that could cause tumors in chickens. He won a Nobel Prize for this discovery in 1966. His experiments clearly demonstrated that some cancers could be caused by infectious agents and led to the discovery of many more cancer-causing viruses in animals as well as humans.
RSV is a retrovirus that contains two copies of a plus-strand RNA genome. Its genome consists of four main open...
RSV is a retrovirus that contains two copies of a plus-strand RNA genome. Its genome consists of four main open...
5.1K