The relationship of coffee consumption with mortality
Esther Lopez-Garcia1, Rob M van Dam, Tricia Y Li
1Harvard School of Public Health, Brigham and Women's Hospital, Boston, Massachussetts, USA. esther.lopez@uam.es
Annals of Internal Medicine
|June 19, 2008
Summary
Regular coffee consumption is not linked to increased mortality. Moderate coffee intake may offer a modest benefit for all-cause and cardiovascular disease mortality, warranting further investigation.
Area of Science:
- Epidemiology
- Nutritional Science
- Public Health
Background:
- Limited data exist on coffee's impact on mortality.
- Coffee consumption is widespread and associated with diverse health effects.
Purpose of the Study:
- To investigate the association between coffee consumption and mortality from cardiovascular disease (CVD), cancer, and all causes.
- To analyze sex-specific mortality risks over extended follow-up periods.
Main Methods:
- Prospective cohort study utilizing Cox proportional hazard models.
- Analysis of data from the Health Professionals Follow-up Study and Nurses' Health Study.
- Assessment of coffee consumption and mortality in over 120,000 men and women.
Main Results:
- Coffee consumption showed an inverse association with all-cause mortality in both men and women, particularly with higher intake levels.
- This association was primarily driven by a reduced risk of cardiovascular disease (CVD) mortality and was independent of caffeine.
- No significant association was found between coffee consumption and cancer mortality.
Conclusions:
- Regular coffee consumption does not increase mortality risk.
- A potential modest benefit of coffee on all-cause and CVD mortality warrants further research.
- Self-reported coffee intake may introduce measurement error.
Related Concept Videos
Hypothesis Test for Test of Independence
7.4K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
7.4K
Cancer Survival Analysis
645
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...
645
Cause and Effect
12.0K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
12.0K
Comparing the Survival Analysis of Two or More Groups
548
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...
548
Statistical Methods for Analyzing Epidemiological Data
889
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:
889
Assumptions of Survival Analysis
391
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.
391


