ArticleEpidemiology (Cambridge, Mass.)2025
Addressing Measurement Error in Intimate Partner Violence Self-report Data Using Multiple Overimputation and Multidimensional Quantitative Bias Analysis.
Article in Epidemiology (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Intimate partner violence victimisation before, during and after the COVID-19 pandemic in Manitoba, Canada: a time series analysis.BMJ public health · 2026Article
- Understanding Intimate Partner Violence Through Police Crime Data: Descriptive and Temporal Insights.Behavioral sciences (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundIntimate partner violence (IPV) is an important global health issue for which measurement error limits public health action. Although most national IPV prevalence estimates come from general health surveys like the Demographic and Health Surveys (DHS), such data probably underestimate prevalence compared with violence-focused surveys.
methodsUsing violence-focused surveys conducted in the same country and year (±1) as validation data, we explored two methods of bias adjustment to address measurement error in DHS prevalence estimates. In multidimensional bias analysis, we directly adjusted summary prevalence estimates, using a range of possible sensitivities (10%-100%) and specificities (95%-100%) to elucidate their reasonable bounds. In multiple overimputation, we reestimated all IPV observations, incorporating prior information on measurement error, and averaged prevalence estimates over 50 iterations.
resultsMultidimensional bias analysis revealed that an assumption of 95% specificity resulted in negative prevalence estimates in some cases, confirming that false positives are likely negligible. Reasonable sensitivities varied considerably across countries and IPV types, likely due to differences in the number of items used to assess IPV. Multiple overimputation-adjusted estimates were similar to survey estimates, except when unadjusted DHS estimates were <5% and highly discrepant. Past-year estimates were less discrepant than lifetime estimates, suggesting that recall bias may be a factor in underreporting.
conclusionThis study examines measurement error due to IPV underreporting in specific contexts where external information exists, highlighting the need for more accurate IPV assessment using multiple items per domain and for internal validation studies to be incorporated into large-scale surveys.
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