Related Experiment Video

Updated: Jun 18, 2026

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

Breast cancer mortality in Mexico: an age-period-cohort analysis

Francisco Franco-Marina¹, Eduardo Lazcano-Ponce, Lizbeth López-Carrillo

¹Instituto Nacional de Rehabilitación, División de Epidemiología, Calz. México-Xochimilco 289 Col.Arenal de Guadalupe, Tlalpan, 14389 México, DF. franciscofrancomarina@gmail.com

Salud Publica De Mexico

|December 8, 2009

Abstract

Objective:

To assess the age, period and cohort effects on breast cancer (BC) mortality in Mexico.

Material And Methods:

Age, period and cohort curvature trends for BC mortality were estimated through the Poisson Regression model proposed by Holford.

Results:

Nationally, BC death rates have leveled off since 1995 in most age groups. BC mortality trends are mainly determined by birth cohort and age effects in Mexico. Women born between 1940 and 1955 show the highest rate of increase in BC mortality. Women born afterwards still show an increasing trend but at a much lower rate. Mammography and adjuvant therapy have had a limited impact on mortality. Potential reasons for observed patterns are discussed. An increase in BC mortality in Mexico is expected in the following decades.

Conclusions:

Mammography screening programs and timely access to effective treatment should be a national priority to reverse the expected increasing BC mortality trend.

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...

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 Cox...

Prevalence and Incidence

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

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:

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

Alcohol consumption and oral human papillomavirus infection among men living with HIV: a cross-sectional study from the ULACNet 201 trial.

Scientific reports·2026

Knowledge, attitudes, and practices related to adult vaccination among adults and healthcare professionals across Mexico.

PloS one·2026

Genotype distribution of human papillomavirus (HPV) types in oral gargle specimens among men living with HIV in Mexico, Brazil, and Puerto Rico: A cross-sectional study.

The Journal of infection·2026

Association of alcohol consumption with oral high-risk human papillomavirus infection: a cross-sectional study within the multinational HPV infection in men (HIM) cohort.

Lancet regional health. Americas·2026

Extended HPV typing as an efficient alternative within HPV-based screening programs.

International journal of cancer·2026

Prevalence of social isolation and loneliness by living arrangement among Mexican elderly during the Covid-19 pandemic.

Salud publica de Mexico·2026

See all related articles