Population-Based Modeling of Cholesterol Lowering in the United States
To evaluate the cost-effectiveness of cholesterol-lowering strategies in the United States population. The study used the Coronary Heart Disease (CHD) Policy Model, a state-transition computer simulation model used to obtain forecasts of the public health impact and economic cost of CHD in the United States population.
Coronary Heart Disease Risk Reduction
|Study Design:||Observational Model: Natural History|
|Study Start Date:||August 1991|
|Estimated Study Completion Date:||May 1993|
The study was part of an Institute-initiated Request for Applications (RFA) titled "Cost-Effective Strategies of Cholesterol-Lowering" released by the NHLBI in 1990. The RFA was stimulated by the controversy concerning costs and cost-effectiveness that followed the 1987 report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. The RFA was intended to support a broad and thorough quantitative exploration of the potential health benefits and costs of cholesterol-lowering from multiple perspectives.
The study added to the CHD Policy Model the capability to model the consequences of reductions in LDL cholesterol and increases in HDL/LDL ratios in the United States population. The CHD Policy Model was used for several studies, including: to compare the implications of using alternative epidemiologic studies as the basis for estimating the association between cholesterol levels and CHD risk; to derive cutting points for initiating cholesterol reduction, specific to age, sex, and CHD risk factors, and based on cost-effectiveness criteria; to compare the cost-effectiveness of specific targeted and population-wide strategies for cholesterol reduction; to incorporate the effects of treatments on quality of life, including both adverse effects of cholesterol-lowering drugs and reductions in CHD morbid events; and finally, to perform a cost-effectiveness analysis of cholesterol screening, incorporating costs of screening, effects of measurement error on misclassification of patients, and variations in individual cholesterol levels over time.