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