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An alternative procedure for fitting relational model life tables.

|January 1, 1995

Summary

This study presents a new method for fitting relational life tables to mortality data lacking infant and child mortality estimates. The technique provides a more balanced weighting of age-specific mortality risks, improving demographic analysis.

Keywords:

Age Specific Death Rate Americas Brazil Data Collection Data Sources Death Rate Demographic Analysis Demographic Factors Developing Countries Estimation Technics Latin America Life Table Method Life Tables Measurement Mortality Population Population Dynamics Reliability Research Methodology South America

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Area of Science:

Demography
Mortality Analysis
Statistical Modeling

Background:

Challenges in constructing life tables arise when infant and child mortality data are unreliable or absent.
Such data deficiencies are common in populations with indirect adult mortality estimation (e.g., orphanhood methods) or incomplete vital registration.
Traditional methods often overemphasize data from younger age groups, potentially skewing results.

Purpose of the Study:

To develop and present a novel technique for fitting relational model life tables.
To address the specific problem of incomplete mortality data, particularly the lack of infant and child mortality estimates.
To offer an alternative fitting method that provides a more equitable weighting of available age-specific mortality data.

Main Methods:

The study proposes a new fitting technique for relational model life tables.
This method allows for equal weighting of all available age-specific mortality risk information.
The technique is applied to mortality data where infant and child mortality estimates are not reliable.

Main Results:

The proposed fitting technique offers an improvement over traditional methods by not disproportionately weighting younger age data.
It enables a more balanced utilization of all available age-specific mortality risk information.
An application using Brazilian demographic data demonstrates the practical utility of the method.

Conclusions:

The developed fitting technique is effective for constructing life tables with incomplete mortality data.
This approach enhances the reliability of demographic analyses in situations with data limitations.
The method provides a valuable tool for demographers and public health researchers working with challenging datasets.