Addressing measurement error in intimate partner violence self-report data using multiple overimputation and multidimensional quantitative bias analysis.

BACKGROUND

Intimate 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 to violence-focused surveys.

METHODS

Using 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 re-estimated all IPV observations, incorporating prior information on measurement error, and averaged prevalence estimates over 50 iterations.

RESULTS

Multidimensional 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.

CONCLUSIONS

This 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.