Related Experiment Video
Updated: Jun 4, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.4K
Beyond the underlying cause of death: an algorithm to study multi-morbidity at death
Francesco Grippo1, Luisa Frova2, Marilena Pappagallo2
1Istituto Nazionale di Statistica (ISTAT), Viale Liegi, 13, 00198, Rome, Italy. frgrippo@istat.it.
Population Health Metrics
|December 19, 2024
Summary
Analyzing multiple causes of death reveals variations in multi-morbidity across countries. Our algorithm helps understand these differences by assigning roles to each cause listed on death certificates.
Area of Science:
- Public Health
- Epidemiology
- Gerontology
Background:
- Increasing life expectancy leads to higher prevalence of multi-morbidity.
- Cause-of-death data can monitor multi-morbidity, but requires analyzing all listed causes, not just the underlying one.
Purpose of the Study:
- To develop and apply an algorithm for classifying the role of each cause on death certificates.
- To analyze multi-cause-of-death data to understand cross-country variations in multi-morbidity.
Main Methods:
- Developed an algorithm based on WHO rules to assign roles (originating, precipitating, associated, ill-defined) to causes on death certificates.
- Applied the algorithm to death records from Italy, France, Spain, and the US in 2017.
Main Results:
- Average number of originating causes was similar across countries.
- Proportion of death certificates with multiple originating causes (multi-morbidity) ranged from 10% (US) to 18% (Spain).
- Proportion of deaths with associated causes was higher in Italy (41%) and US (42%) compared to France (29%) and Spain (27%).
Conclusions:
- The algorithm clarifies cross-country differences in the average number of causes on death certificates.
- Findings highlight the need for improved multiple cause-of-death analysis methods to better understand multi-morbidity.
Related Concept Videos
01:20Actuarial Approach
61
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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
61
01:20Comparing the Survival Analysis of Two or More Groups
149
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...
149
01:21Cancer Survival Analysis
328
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...
328
01:24Kaplan-Meier Approach
94
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,...
94
01:18Introduction To Survival Analysis
180
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
180
01:15Assumptions of Survival Analysis
96
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.
96