OBJECTIVES: To determine through a literature review how economic evaluations incorporate utility data into models when estimating long-term cost-effectiveness of lipid lowering therapies in primary and/or secondary cardiovascular disease (CVD) prevention. METHODS: We used MEDLINE to identify published articles reporting cost-effectiveness models.  Inclusion criteria included: English language articles published from 2000-2013, studies in adults ≥18 years old, studies assessing statins or ezetimibe for primary and/or secondary CVD prevention, and studies in the US, Canada, UK, Spain, Germany, Australia, Sweden, France, and Italy.  Two researchers independently identified articles and disagreements were resolved by consensus.  RESULTS: Sixty-one studies fulfilled the inclusion criteria.  Forty-eight studies assessed the long-term cost-effectiveness (≥10 years) of lipid-lowering therapy— 20 studies were in primary CVD prevention, 19 were in secondary prevention, and 9 were in both primary and secondary prevention.  36/48 studies incorporated utility parameters into their models.  23/36 models differentiated short-term versus long-term utility impact of cardiovascular events.  Eleven models were able to differentiate between short-term and long-term utilities through model structure, where separate health states were created for events versus post-events (e.g., stroke versus post-stroke), and utility values could be assigned to these health states accordingly.  Twelve models were able to differentiate short-term versus long-term utilities by changing the model inputs, where different utility values were applied to a health state according to length of time after the event (e.g., event year versus subsequent years for a health state, or <6 months versus >6 months from time of event).  Eleven models accounted for the occurrence of multiple events by applying disutilities or combining utilities multiplicatively for patients experiencing ≥1 event over time. CONCLUSIONS: Around two-thirds of the published long-term models differentiated short-term versus long-term utility impacts of cardiovascular events through model structure or utility inputs, which should be incorporated into future models on this topic.