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Using interviewer random effects to remove selection bias from HIV prevalence estimates.

Mark E McGovern1,2, Till Bärnighausen3,4, Joshua A Salomon5

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This study introduces a new method to correct selection bias in HIV prevalence estimates, particularly when individuals avoid testing. The approach provides more accurate HIV prevalence data, even with high non-participation rates.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Selection bias can occur in HIV prevalence estimates if individuals who are HIV positive are less likely to participate in testing.
  • Traditional imputation methods may yield biased results when non-participation is linked to unobserved characteristics like HIV status.

Purpose of the Study:

  • To develop and validate a novel statistical method to correct for selection bias in HIV prevalence estimation.
  • To provide more accurate and reliable HIV prevalence estimates, especially in surveys with significant non-participation.

Main Methods:

  • Utilized Heckman-type selection models incorporating interviewer identity as a selection variable.
  • Introduced a new random effects method to address non-convergence issues in existing selection models.
  • Enabled the construction of bootstrapped standard errors to account for uncertainty in the estimated relationship between testing and HIV status.

Main Results:

  • Applied the method to Demographic and Health Surveys data from Ghana (2003) and Zambia (2007).
  • In Ghana, HIV prevalence estimates showed little evidence of selection bias (1.4% vs. 1.6% observed).
  • In Zambia, the method indicated significant selection bias, with those declining tests being more likely HIV positive (16.3% estimated vs. 12.1% observed).

Conclusions:

  • The proposed approach effectively corrects for selection bias in HIV prevalence estimates.
  • The method is applicable across a range of HIV prevalence and non-participation levels.
  • It offers a practical solution for incorporating sampling and parameter uncertainty into confidence interval estimation, crucial for accurate public health assessments.