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Estimating hidden populations by transferring knowledge from geographically misaligned levels.

Douglas R M Azevedo1, Marcos O Prates1, Renato M Assunção2

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Estimating hidden populations, like private health insurance users in Brazil, is challenging. This study introduces a flexible model to improve sub-population size estimations using varied data sources and geographical levels.

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Area of Science:

  • Biostatistics
  • Public Health
  • Statistical Modeling

Background:

  • Estimating hidden sub-populations is crucial for various fields, including public health planning.
  • Brazil's public health planning relies on understanding the number of individuals with private health insurance.
  • Data on sub-populations often exists at different geographical levels, posing an estimation challenge.

Purpose of the Study:

  • To propose a novel statistical model for estimating hidden sub-population sizes.
  • To develop a flexible family of link functions robust to model departures.
  • To enable accurate sub-population size estimation at any desired geographical level.

Main Methods:

  • Utilizing individual-level data to model the dependence between response and explanatory variables.
  • Proposing a family of link functions with flexible asymptotes.
  • Applying the fitted model to estimate sub-population sizes across different geographical scales.

Main Results:

  • The proposed model effectively learns dependencies from individual data.
  • Flexible link functions accommodate real-world data complexities and are robust.
  • The methodology successfully estimates hidden sub-population sizes at local levels.

Conclusions:

  • The developed statistical model offers a robust approach for hidden sub-population estimation.
  • This method enhances public health planning by providing accurate local estimates.
  • The study successfully illustrates the methodology in estimating Brazil's public health system users.