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Extending cluster lot quality assurance sampling designs for surveillance programs.

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  • 1Department of Family and Community Medicine, University of New Mexico, 2400 Tucker Avenue Northeast Albuquerque, NM 87131, U.S.A.

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Summary

Lot quality assurance sampling (LQAS) methods are enhanced for global health surveillance. This study introduces a flexible, nonparametric procedure to incorporate clustering effects into LQAS sample designs, improving accuracy for malnutrition monitoring.

Keywords:
LQASacceptance samplinglongitudinal surveillancenutritionsurvey design

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Area of Science:

  • Public Health
  • Survey Methodology
  • Biostatistics

Background:

  • Lot quality assurance sampling (LQAS) is widely used for rapid surveillance in global health, classifying areas based on indicator performance.
  • Traditional LQAS surveys often use simple random sampling, which can be less cost-effective than cluster designs for large-scale surveillance.
  • Incorporating clustering is crucial for accurate sample size determination in geographically dispersed populations.

Purpose of the Study:

  • To develop a flexible, nonparametric procedure for Lot Quality Assurance Sampling (LQAS) that accounts for clustering effects.
  • To provide a framework for principled selection of survey design parameters in longitudinal surveillance programs.
  • To design surveys for detecting increases in malnutrition prevalence in nutrition surveillance programs, using historical data and accounting for village-level clustering.

Main Methods:

  • Developed a nonparametric procedure to integrate clustering into LQAS sample size calculations.
  • Applied survey sampling principles to a binary classification framework for LQAS.
  • Utilized historical data and previous survey results to inform new survey designs.
  • Designed cluster-based LQAS surveys for malnutrition surveillance in Kenya and South Sudan.

Main Results:

  • The proposed nonparametric procedure effectively incorporates clustering into LQAS sample designs.
  • The framework allows for appropriate inflation of sample sizes to accommodate finite cluster populations.
  • The methodology was successfully applied to design surveys for detecting malnutrition prevalence spikes.
  • The approach demonstrated the ability to detect rises in childhood malnutrition rates by combining historical and current data.

Conclusions:

  • The developed nonparametric procedure offers a simple and flexible method to enhance LQAS for clustered populations.
  • This framework supports principled survey design for longitudinal surveillance, particularly in resource-limited settings.
  • The application in Kenya and South Sudan highlights the utility of the method for timely malnutrition monitoring and intervention planning.