Received from the Division of General Internal Medicine, University of Michigan (CK, TPH, EAK) and the Ann Arbor VA Center for Practice Management and Outcomes Research, VA Ann Arbor Healthcare System (TPH, EAK), Ann Arbor, Mich.
Review of Evidence and Explanations for Suboptimal Screening and Treatment of Dyslipidemia in Women
A Conceptual Model
Article first published online: 3 OCT 2003
Journal of General Internal Medicine
Volume 18, Issue 10, pages 854–863, October 2003
How to Cite
Kim, C., Hofer, T. P. and Kerr, E. A. (2003), Review of Evidence and Explanations for Suboptimal Screening and Treatment of Dyslipidemia in Women. Journal of General Internal Medicine, 18: 854–863. doi: 10.1046/j.1525-1497.2003.20910.x
- Issue published online: 3 OCT 2003
- Article first published online: 3 OCT 2003
Screening and treatment rates for dyslipidemia in populations at high risk for cardiovascular disease (CVD) are inappropriately low and rates among women may be lower than among men. We conducted a review of the literature for possible explanations of these observed gender differences and categorized the evidence in terms of a conceptual model that we describe. Factors related to physicians’ attitudes and knowledge, the patient's priorities and characteristics, and the health care systems in which they interact are all likely to play important roles in determining screening rates, but are not well understood. Research and interventions that simultaneously consider the influence of patient, clinician, and health system factors, and particularly research that focuses on modifiable mechanisms, will help us understand the causes of the observed gender differences and lead to improvements in cholesterol screening and management in high-risk women. For example, patient and physician preferences for lipid and other CVD risk factor management have not been well studied, particularly in relation to other gender-specific screening issues, costs of therapy, and by degree of CVD risk; better understanding of how available health plan benefits interact with these preferences could lead to structural changes in benefits that might improve screening and treatment.