Affiliations: [a] Center for Financing, Access and Cost Trends, Agency
for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850, USA.
Tel.: +1 301 427 1466; Fax: +1 301 427 1276; E-mail:
scohen@ahrq.gov
| [b] Division of Statistical Research and Methods, Center
for Financing, Access and Cost Trends, Agency for Healthcare Research and
Quality, 540 Gaither Road, Rockville, MD 20850, USA
Correspondence:
[*]
Corresponding author
Abstract: In this paper, an evaluation model is presented to assess the utility of
probabilistic models in terms of their capacity to successfully oversample
policy relevant population subgroups that are subject to transitions. Examples
of these applications are drawn from the Medical Expenditure Panel Survey
(MEPS). Given the high concentration of health care expenditures in a given year
among a relatively small percentage of the population, a prediction model that
can accurately identify the persistence of high levels of expenditures is an
important analytical tool. This type of modeling effort also enhances the
ability to discern the causes of high health care expenses and the
characteristics of the individuals who incur them. This feature also applies to
prediction models that can accurately identify those individuals with
persistently low or average levels of expenditures. The models that are
presented have particular relevance as statistical tools to facilitate efficient
sampling strategies that permit the selection of an over-sample of individuals
likely to incur high levels of medical expenditures in the future.
