Related Experiment Video
Updated: May 9, 2026

10:00
Measurement of Lifespan in Drosophila melanogaster
Published on: January 7, 2013
Calculating disability-adjusted-life-years lost (DALYs) in discrete-time
1Department of International Health, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, USA. blarson@bu.edu.
Cost Effectiveness and Resource Allocation : C/E
|August 10, 2013
Summary
This paper simplifies calculating disability-adjusted life-years lost (DALYs) using a discrete time method, making complex cost-effectiveness analysis accessible for public health students and practitioners.
Area of Science:
- Public Health
- Health Economics
- Epidemiology
Background:
- Disability-Adjusted Life-Years (DALYs) are crucial for cost-effectiveness analysis.
- Existing DALY calculation equations can be complex and difficult to understand for many practitioners.
- A clear, teachable method for DALY calculation is needed.
Purpose of the Study:
- To present a discrete time formulation for calculating DALYs.
- To provide an accessible method for public health students and practitioners.
- To ensure consistency with standard discounting methods in cost-effectiveness analysis.
Main Methods:
- Development of a discrete time formulation for DALY calculation.
- Derivation of a continuous-time adjustment factor for precision.
- Illustration using both a new simple example and a previously published example.
Main Results:
- A straightforward discrete time method for DALY calculation is demonstrated.
- The proposed method is easy to teach and apply for individuals with basic discounting skills.
- The discrete method is shown to be consistent with continuous-time approaches.
Conclusions:
- The discrete time formulation simplifies DALY calculations for cost-effectiveness analysis.
- This approach enhances the understanding and application of DALYs in public health.
- The method provides a practical tool for health economic evaluations.
Related Concept Videos
Life Tables
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Actuarial Approach
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,...
Applications of Life Tables
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Introduction To Survival Analysis
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 until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
Hazard Rate
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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,...

