Community preferences for health states associated with intimate partner violence

Med Care. 2006 Aug;44(8):738-44. doi: 10.1097/01.mlr.0000215860.58954.86.


Background: One in 4 women is affected by intimate partner violence in her lifetime. This article reports on a cross-sectional survey to estimate community preferences for health states resulting from intimate partner violence.

Methods: A secondary analysis was conducted of data from a convenience sample of 93 abused and 138 nonabused women (231 total) recruited for in-person interviews from hospital outpatient department waiting rooms in metropolitan Boston, Massachusetts. SF-12 data were converted to utilities to describe community-perspective preferences for health states associated with intimate partner violence. Linear regression analysis was used to explore the association between violence and utility while controlling for other health and demographic factors.

Results: Median utility for intimate partner violence was between 0.58 and 0.63 on a scale of 0 (equivalent to death) to 1.0 (equivalent to optimal health), with a range from 0.64 to 0.66 for less severe violence to 0.53 to 0.62 for more severe violence. The data do not reveal whether violence itself is responsible for lower utility or whether a constellation of factors contributes to disutility experienced by women victims of abuse.

Discussion: The utility of health states experienced by women exposed to intimate partner violence is substantially diminished compared with optimal health and even other health conditions. These values quantify the substantial negative health impact of the experience of intimate partner violence in terms that allow comparison across diseases. They can be used in cost-effectiveness analyses to identify the benefits and potential returns from resources allocated to violence prevention and intervention efforts.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Boston
  • Cross-Sectional Studies
  • Domestic Violence*
  • Female
  • Health Status*
  • Humans
  • Interviews as Topic
  • Linear Models
  • Quality of Life