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Addressing Measurement Error in Intimate Partner Violence Self-report Data Using Multiple Overimputation and

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Area of Science:

  • Public Health
  • Epidemiology
  • Survey Methodology

Background:

  • Intimate partner violence (IPV) is a significant global health concern.
  • Measurement error in national surveys like DHS likely underestimates IPV prevalence.
  • Accurate IPV data is crucial for effective public health interventions.

Purpose of the Study:

  • To explore bias adjustment methods for correcting measurement error in DHS IPV prevalence estimates.
  • To assess the impact of underreporting on IPV prevalence data.
  • To provide insights into improving the accuracy of IPV data collection.

Main Methods:

  • Utilized violence-focused surveys for validation data.
  • Applied multidimensional bias analysis with varying sensitivities and specificities.
  • Employed multiple overimputation techniques to re-estimate IPV observations.

Main Results:

  • Multidimensional bias analysis indicated negligible false positives with 95% specificity.
  • Sensitivities varied by country and IPV type, influenced by the number of assessment items.
  • Multiple overimputation yielded similar estimates to survey data, except for low prevalence discrepancies.
  • Past-year estimates showed less underreporting than lifetime estimates, suggesting recall bias.

Conclusions:

  • Measurement error from IPV underreporting necessitates context-specific examination.
  • Accurate IPV assessment requires multiple items per domain.
  • Internal validation studies should be integrated into large-scale surveys for improved data quality.