|
Article Excerpt
Cost-of-illness (COI) studies are increasingly used to quantify the public health burden associated with a disease, illness, injury, or risk factor (e.g., Gross et al. 1999; American Diabetes Association [ADA] 2003; Fishman et al. 2003; Honeycutt et al. 2003; Finkelstein et al. 2005). These studies estimate the costs associated with a disease, including direct medical costs for diagnosing and treating the disease and indirect costs, such as productivity losses. Quantifying the economic and public health burden of a disease is useful for understanding the impact of one disease relative to others and for establishing priorities for disease treatment and prevention (Rice 1994). In some cases, COI estimates are broken down to show the distribution of disease costs across payers (e.g., Finkelstein, Fiebelkorn, and Wang 2003), which can help demonstrate the burden borne by specific stakeholders.
COI studies often attempt to estimate the disease-attributable costs that could be avoided if a case of the disease were prevented. Some COI analyses estimate annual costs for the prevalent population, whereas others estimate lifetime costs for the incident population. The two main approaches for estimating prevalence-based disease-attributable costs, the focus of this analysis, are (1) a regression-based (RB) approach applied to individual-level cost data and (2) an attributable fraction (AF) approach applied to aggregate cost data (Miller, Ernst, and Collin 1999). Although we focus on costs attributable to disease, the same approaches can be applied to estimate the costs of risk factors, such as obesity.
The RB approach uses regression analysis to estimate models of medical spending. These models include an indicator variable for the disease of interest and control for individual-level characteristics, such as sociodemographic variables and comorbidities. The coefficient estimates from these models are used to predict individual-level health care spending in the disease population and then to predict health care spending for these individuals if the disease were eliminated (i.e., treating the disease indicator variable as equal to zero). The mean of the difference between these two predicted values provides an estimate of per-person medical spending attributable to the disease.
The AF approach involves identifying the medical conditions that are caused by the disease of interest and obtaining estimates of the aggregate cost of each condition. AFs are then calculated using epidemiologic formulas for each condition. The AFs represent the portions of disease prevalence that are caused by the presence of the disease. Disease-attributable costs are estimated by multiplying the AFs by the aggregate cost of each condition, and then summing across all conditions.
The RB and AF approaches have been widely used to estimate costs attributable to disease or risk factors (e.g., Hodgson and Cohen 1999; ADA 2003; Finkelstein, Fiebelkorn, and Wang 2003). The choice of an approach often depends on the available data and how the cost estimates will be used. The RB approach uses individual-level data on health care spending and the presence of disease to assign costs based on a comparison of actual medical spending among people with and without the disease. It therefore captures differences in disease-attributable spending that may be caused by an increased number of health care visits or longer lengths of stay for people with the disease or by higher costs for any particular visit.
The AF approach often includes only diseases known to result from the condition of interest. For example, when disease cost estimates are used to establish damages in legal proceedings, it may be important to clearly define which conditions are attributed to the disease. Disease cost estimates prepared in support of tobacco litigation in the late 1990s frequently used an AF approach, because analysts could specify that costs include only those diseases known to be caused by smoking. However, several recent AF analyses have included the cost of general medical conditions (e.g., Miller, Ernst, and Collin 1999; Coller, Harrison, and McInnes 2002; ADA 2003), which allows for the possibility that the disease leads to higher overall medical spending and raises costs to treat conditions not generally thought to be caused by the disease.
Even if the cost of general medical conditions is included, the AF approach may produce lower estimates than the RB approach, because it uses aggregate disease cost data. In the case of diabetes, using aggregate data to estimate medical costs implicitly assumes that treating a nondiabetes event (e.g., cardiovascular disease [CVD]) costs the same for a person with diabetes as it does for a similar person without diabetes. But the person with diabetes might require a longer hospital stay for the nondiabetes event, because diabetes complicates treatment and raises treatment costs. By not accounting for differences in treatment intensity, the AF approach may underestimate attributable costs.
In this study, we use both RB and AF approaches to estimate diabetes-attributable medical spending.
METHODS
Data
Study data were drawn from the Medical Expenditure Panel Survey (MEPS) for 1998 through 2003. MEPS is a nationally representative survey of the U.S. civilian, noninstitutionalized population administered by the Agency for Healthcare Research and Quality (AHRQ). Household respondents provided demographic information, self-reported medical conditions, and medical expenditure and utilization information for medical events. For some individuals, self-reported medical expenditures are supplemented with information from medical providers and insurers. MEPS uses a complex survey design and contains population weights to create nationally representative estimates (Agency for Healthcare Research and Quality 2000).
To ensure sufficient sample size for our analysis, we pooled 6 years of data from the consolidated, medical conditions, and individual event files (consisting of office-based visits, hospital inpatient stays, outpatient department visits, emergency room visits, prescribed medicines, and home health tiles). Because MEPS is an overlapping panel...
|
