Issues of Data Capacity and Statistical Quality to Support Health Care Modeling and Microsimulation Efforts
There is a growing demand for timely, high quality and precise estimates of health care parameters at the national and sub-national levels, and associated readily accessible data resources to inform health care policy and practice. Existing sentinel health care databases that provide nationally representative population based data on measures of health care access, cost, use, health insurance coverage, health status and health care quality, provide the necessary foundation to support descriptive and behavioral analyses of the U.S. health care system. Such studies help inform assessments of the availability and costs of private health insurance in the employment-related and non-group markets; the population enrolled in public health insurance coverage and those without health care coverage; and the role of health status in health care use, expenditures, and household decision making, and in health insurance and employment choices. To complement these assessments of the “current state” of health care, policymakers also depend on model-based estimates of the “future state” under alternative demographic, economic and technological assumptions, which are subject to greater levels of uncertainty traditionally associated with sampling and nonsampling error. Such modeling efforts directed to predicting the “future state” include economic models projecting health care expenditures and utilization, estimating the impact of changes in financing, coverage, and reimbursement policy, and determining who benefits and who bears the cost of a change in policy. Government and non-governmental entities rely upon these data to evaluate health reform policies, the effect of tax code changes on health expenditures and tax revenue, and proposed changes in government health programs such as Medicare. Comparable standards of data quality and statistical integrity for these types of modeling and microsimulation efforts are needed to ensure policymakers have a sound understanding of the level of uncertainty associated with these model-based estimates. This presentation will focus on several of these ongoing health care modeling and microsimulation efforts to characterize sources of uncertainty in the resultant estimates and methodologies that can be employed to better quantify their error bounds.