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This study introduces a new sampling frame model to accurately estimate population health metrics, accounting for aggregation errors. The improved method enhances uncertainty estimates and offers robust results across different population scales.

Keywords:
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Area of Science:

  • Geostatistics
  • Spatial Statistics
  • Demographic Health Surveys

Background:

  • Spatial aggregation estimates population quantities but often ignores aggregation errors.
  • Traditional methods treat populations as fixed density surfaces, overlooking key variations.
  • Existing approaches are susceptible to aggregation grid resolution, impacting interval estimates.

Purpose of the Study:

  • To introduce a sampling frame model that accounts for aggregation error in spatial population estimates.
  • To improve the transparency and simplicity of aggregation models.
  • To enhance the accuracy and robustness of population quantity estimations.

Main Methods:

  • Developed a novel sampling frame model to incorporate aggregation error.
  • Conducted simulation studies using neonatal mortality rate (NMR) data.
  • Compared the proposed model against traditional aggregation approaches.

Main Results:

  • The new model preserves aggregate point estimates while appropriately increasing uncertainties.
  • The proposed approach is robust to aggregation grid resolution, unlike traditional methods.
  • Differences between methods are pronounced in low-population areas and at finer administrative levels.

Conclusions:

  • The sampling frame model offers a more reliable method for spatial population aggregation.
  • The model's resolution robustness is critical for accurate health metric estimation.
  • Findings highlight the importance of accounting for aggregation error in demographic and health research.