Bergenfeld Irina, Richardson Robin A, Hadd Alexandria R, Clark Cari Jo, Haardörfer Regine, Wiltshire Charis, Lash Timothy L, Bengtson Angela M
Department of Global Health, Rollins School of Public Health, Emory University 1518 Clifton Rd Atlanta, GA, US.
Department of Epidemiology, Rollins School of Public Health, Emory University 1518 Clifton Rd Atlanta, GA, US.
Epidemiology. 2025 Jul 4. doi: 10.1097/EDE.0000000000001896.
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.
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.
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.
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.
亲密伴侣暴力（IPV）是一个重要的全球健康问题，测量误差限制了公共卫生行动。尽管大多数国家的IPV患病率估计来自人口与健康调查（DHS）等一般健康调查，但与以暴力为重点的调查相比，此类数据可能低估了患病率。
使用在同一国家和年份（±1）进行的以暴力为重点的调查作为验证数据，我们探索了两种偏差调整方法，以解决DHS患病率估计中的测量误差。在多维度偏差分析中，我们使用一系列可能的敏感度（10%-100%）和特异度（95%-100%）直接调整汇总患病率估计值，以阐明其合理范围。在多次重复插补中，我们重新估计所有IPV观察值，纳入测量误差的先验信息，并在50次迭代中平均患病率估计值。
多维度偏差分析表明，假设特异度为95%在某些情况下会导致负患病率估计值，证实假阳性可能可以忽略不计。合理的敏感度在不同国家和IPV类型之间差异很大，可能是由于用于评估IPV的项目数量不同。多次重复插补调整后的估计值与调查估计值相似，除非未调整的DHS估计值<5%且差异很大。过去一年的估计值比终生估计值的差异小，表明回忆偏差可能是报告不足的一个因素。
本研究在存在外部信息的特定背景下检查了因IPV报告不足导致的测量误差，强调需要在每个领域使用多个项目进行更准确的IPV评估，并将内部验证研究纳入大规模调查。
