Related Experiment Video
Updated: Jun 25, 2025

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
Published on: November 1, 2017
Estimating 1a0 and 4a1 in a Life Table: A Model Approach Based on Newly Collected Data
Julio Romero-Prieto1, Andrea Verhulst2, Michel Guillot3
1Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Abstract:
The average age of infant deaths, a10, and the average number of years lived-in the age interval-by those dying between ages 1 and 5, a41, are important quantities allowing the construction of any life table including these ages. In many applications, the direct calculation of these parameters is not possible, so they are estimated using the infant mortality rate-or the death rate from 0 to 1-as a predictor. Existing methods are general approximations that do not consider the full variability in the age patterns of mortality below the age of 5. However, at the same level of mortality, under-five deaths can be more or less concentrated during the first weeks and months of life, thus resulting in very different values of a10 and a41. This article proposes an indirect estimation of these parameters by using a recently developed model of under-five mortality and taking advantage of a new, comprehensive database by detailed age-which is used for validation. The model adapts to a variety of inputs (e.g., rates, probabilities, or the proportion of deaths by sex or for both sexes combined), providing more flexibility for the users and increasing the precision of the estimates. This fresh perspective consolidates a new method that outperforms all previous approaches.
Related Concept Videos
Life Tables
Applications of Life Tables
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Assumptions of Survival Analysis
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...

