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Related Experiment Video

Updated: May 27, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

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

A census-based method for estimating adult mortality.

S H Preston¹, N G Bennett

¹a Population Studies Center , University of Pennsylvania , Philadelphia , USA.

Population Studies

|November 15, 2011

Summary

This summary is machine-generated.

This study presents a method to convert population age distributions into stationary populations using age-specific growth rates. This allows for the calculation of life table mortality measures, with a focus on life expectancy estimation, even with data errors.

Related Experiment Videos

Last Updated: May 27, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

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

Area of Science:

Demography
Biostatistics
Population Health

Background:

Accurate mortality measurement is crucial for public health and policy.
Existing methods for estimating life expectancy can be sensitive to data errors common in developing countries.

Purpose of the Study:

To develop a method for converting age distributions into stationary populations reflecting current mortality conditions.
To enable the calculation of robust life table mortality measures, particularly life expectancy, from available demographic data.

Main Methods:

Utilizes age-specific growth rates derived from successive censuses to transform an observed age distribution.
Applies the derived stationary population to compute life table mortality measures.
Focuses on life expectancy estimation due to its robustness against common data errors.

Main Results:

The proposed method successfully converts age distributions into stationary populations.
Life expectancy can be reliably estimated from this stationary population, even with data imperfections.
Procedures for correcting errors from differential census coverage and age misstatement are effective.

Conclusions:

The method provides a valuable tool for estimating mortality conditions in closed populations.
It enhances the reliability of life expectancy calculations, especially in data-limited settings.
The approach is validated with data from Sweden, India, and South Korea.