Practitioners need more information about intimate partner violence (IPV) victims’ healthcare use trends. We used a novel data-linkage method and complaint categorization allowing us to evaluate IPV victims healthcare use trends compared to the date of their victimization.
This was a retrospective case series using data-linking techniques cross-referencing databases of Medicaid-eligible women between the ages of 16 and 55 years, an IPV Case Database for 2007 and the Florida State Agency for Healthcare Administration, which tracks hospital inpatient, ambulatory and emergency department (ED) use within the State of Florida. We analyzed resulting healthcare visits 1.5 years before and 1.5 years after the women’s reported IPV offense. Using all available claims data a ‘complaint category’ representing categories of presenting chief complaints was assigned to each healthcare visit. Analysis included descriptive statistics, correlation coefficients between time of offense and visits, and a logistic regression analysis.
The 695 victims were linked with 4,344 healthcare visits in the four-year study period. The victims were young (46% in the 16–25 age group and 79% were younger than 35). Healthcare visits were in the ED (83%) rather than other healthcare sites. In the ED, IPV victims mostly had complaint categories of obstetrics and gynaecology-related visits (28.7%), infection-related visits (18.9%), and trauma-related visits (16.3%). ED use escalated approaching the victim’s date of offense (r=0.59, p<0.0001) compared to use of non-ED sites of healthcare use (r=0.07, p=0.5817). ED use deescalated significantly after date of reported offense for ED visits (r=0.50, p<0.0001) versus non-ED use (r=0.00, p=0.9958). The victims’ age group more likely to use the ED than any other age group was the 36–45 age group (OR 4.67, CI [3.26–6.68]).
IPV victims use the ED increasingly approaching their date of offense. Presenting complaints were varied and did not reveal unique identifiers of IPV victims. This novel method of database matching between claims data and government records has been shown to be a valid way to evaluate healthcare utilization of at-risk populations.