000 03752nab a22003497a 4500
999 _c7657
_d7657
005 20250625151614.0
008 220609s2022 xxu||||| |||| 00| 0 eng d
040 _aAFVC
100 _aMaheu-Giroux, Mathieu
_910741
245 _aA framework to model global, regional, and national estimates of intimate partner violence
_cMathieu Maheu-Giroux, Lynnmarie Sardinha, Heidi Stöckl, Sarah R. Meyer, Arnaud Godin, Monica Alexander and Claudia García-Moreno
260 _bBMC,
_c2022
500 _aBMC Medical Research Methodology, 2022, 22, 159
520 _aBackground: Accurate and reliable estimates of violence against women form the backbone of global and regional monitoring efforts to eliminate this human right violation and public health problem. Estimating the prevalence of intimate partner violence (IPV) is challenging due to variations in case definition and recall period, surveyed populations, partner definition, level of age disaggregation, and survey representativeness, among others. In this paper, we aim to develop a sound and flexible statistical modeling framework for global, regional, and national IPV statistics. Methods: We modeled IPV within a Bayesian multilevel modeling framework, accounting for heterogeneity of age groups using age-standardization, and age patterns and time trends using splines functions. Survey comparability is achieved using adjustment factors which are estimated using exact matching and their uncertainty accounted for. Both in-sample and out-of-sample comparisons are used for model validation, including posterior predictive checks. Post-processing of models’ outputs is performed to aggregate estimates at different geographic levels and age groups. Results: A total of 307 unique studies conducted between 2000–2018, from 154 countries/areas, and totaling nearly 1.8 million unique women responses informed lifetime IPV. Past year IPV had a similar number of studies (n = 332), countries/areas represented (n = 159), and individual responses (n = 1.8 million). Roughly half of IPV observations required some adjustments. Posterior predictive checks suggest good model fit to data and out-of-sample comparisons provided reassuring results with small median prediction errors and appropriate coverage of predictions’ intervals. Conclusions: The proposed modeling framework can pool both national and sub-national surveys, account for heterogeneous age groups and age trends, accommodate different surveyed populations, adjust for differences in survey instruments, and efficiently propagate uncertainty to model outputs. Describing this model to reproducible levels of detail enables the accurate interpretation and responsible use of estimates to inform effective violence against women prevention policy and programs, and global monitoring of elimination efforts as part of the Sustainable Development Goals. (Authors' abstract). Record #7657
650 2 7 _aDOMESTIC VIOLENCE
_9203
650 2 0 _aINTERNATIONAL COMPARISON
_93394
650 2 0 _aINTIMATE PARTNER VIOLENCE
_9431
650 2 7 _9455
_aPREVALENCE
650 2 7 _aRESEARCH METHODS
_9499
650 2 7 _aSTATISTICS
_9575
650 2 0 _aVIOLENCE AGAINST WOMEN
_93088
651 _aINTERNATIONAL
_93624
700 _aSardinha, LynnMarie
_98024
700 _aStöckl, Heidi
_99998
700 _aMeyer, Sarah R.
_910742
700 _aGodin, Arnaud
_910931
700 _aAlexander, Monica
_910932
700 _aGarcia-Moreno, Claudia
_91200
773 0 _tBMC Medical Research Methodology, 2022, 22, 159
830 _aBMC Medical Research Methodology
_910933
856 _uhttps://doi.org/10.1186/s12874-022-01634-5
_yDOI: 10.1186/s12874-022-01634-5 (Open access)
942 _2ddc
_cARTICLE
_hpānui-111