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Cluster sampling for immunization coverage.

Social Science & Medicine (1982)

|January 1, 1986

Summary

The World Health Organization Expanded Programme on Immunization (WHO/EPI) cluster sampling is often impractical for dispersed populations. A modified random cluster sampling method is presented to address bias and improve immunization coverage surveys in these settings.

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Area of Science:

Public Health
Epidemiology
Biostatistics

Background:

The World Health Organization Expanded Programme on Immunization (WHO/EPI) utilizes cluster sampling for immunization coverage surveys.
Standard EPI cluster sampling, based on village frameworks, is often impractical in geographically dispersed populations prevalent in many African nations.
Applying the standard method in such settings can lead to significant, unacceptable bias, typically overestimating actual immunization coverage.

Purpose of the Study:

To address the limitations of the standard WHO/EPI cluster sampling method in dispersed populations.
To present a modified random cluster sampling approach suitable for diverse geographical settings.
To reduce bias and improve the accuracy of immunization coverage estimates in challenging environments.

Main Methods:

A modified random cluster sampling methodology was developed.
The proposed method is designed to be practical for dispersed populations.
The study focuses on adapting existing cluster sampling principles to overcome geographical barriers.

Main Results:

The modified method offers a practical alternative to the standard WHO/EPI approach for dispersed populations.
This adaptation aims to mitigate the overestimation bias inherent in applying the standard method.
The presented technique enhances the feasibility and accuracy of immunization surveys in challenging terrains.

Conclusions:

A modified random cluster sampling method is essential for accurate immunization coverage surveys in dispersed populations.
This adaptation improves the reliability of data collection in diverse geographical contexts.
The proposed method provides a viable solution for EPI programmes operating in resource-limited and geographically challenging areas.

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