Maternal and Child Health Journal

, Volume 16, Issue 5, pp 1131–1137 | Cite as

Intimate Partner Violence Affects Skilled Attendance at Most Recent Delivery Among Women in Kenya

  • Leslie Goo
  • Siobán D. Harlow


Delivery assistance by skilled health personnel is a key progress indicator for Millennium Development Goal 5, which aims to reduce the worldwide maternal mortality ratio by 75% between 1990 and 2015. The role of socio-demographic factors in determining skilled attendance at delivery has been widely explored, but relatively little attention has been paid to the effect of gender power relations on delivery care. This analysis investigated whether women’s status in the household, as measured by their experience of intimate partner violence (IPV), affected skilled attendance at most recent delivery among women in Kenya. Cross-sectional data were obtained from the 2003 Kenya Demographic and Health Surveys (KDHS). 975 ever-married women who had given birth in the past year and completed the KDHS domestic violence module were included in the analysis. Logistic regression was used to assess the association between skilled attendance and IPV. In this sample, 46% reported having experienced any type of IPV, with 39% reporting physical violence, 21% emotional violence, and 13% sexual violence. After adjusting for demographic characteristics and number of antenatal visits, lifetime experience of emotional violence was found to decrease the odds of skilled attendance at most recent delivery by 40%, while lifetime experience of physical violence reduced the odds by 29%. Women’s experience of IPV may influence receipt of skilled attendance during parturition, and should be addressed as national programs and their international partners align efforts to contribute to the achievement of Millennium Development Goal 5.


Intimate partner violence Skilled attendance Maternal mortality 



We are grateful to the Measure DHS for providing access to this dataset, and to Noureddine Abderrahim of Measure DHS for assistance with interpretation of variable codes. We would also like to thank Kristin Dunkle and Lynda Lisabeth for comments on the manuscript, and Pangaja Paramsothy for performing goodness-of-fit tests on the final regression models.

Conflicts of interest



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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Graduate Program in Pathobiology, Department of Global HealthUniversity of WashingtonSeattleUSA
  2. 2.Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborUSA

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