Joel W. Cohen

Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates

In 1998 the medical costs of obesity were estimated to be as high as

The Medical Expenditure Panel Survey: A National Information Resource to Support Healthcare Cost Research and Inform Policy and Practice

78.5 billion, with roughly half financed by Medicare and Medicaid. This analysis presents updated estimates of the costs of obesity for the United States across payers (Medicare, Medicaid, and private insurers), in separate categories for inpatient, non-inpatient, and prescription drug spending. We found that the increased prevalence of obesity is responsible for almost

Using the SF-12 health status measure to improve predictions of medical expenditures

40 billion of increased medical spending through 2006, including

State- and payer-specific estimates of annual medical expenditures attributable to obesity.

7 billion in Medicare prescription drug costs. We estimate that the medical costs of obesity could have risen to

Sensitivity of household reported medical conditions in the medical expenditure panel survey.

147 billion per year by 2008.

Using information on clinical conditions to predict high-cost patients.

Background:The Medical Expenditure Panel Survey (MEPS) collects detailed information regarding the use and payment for health care services from a nationally representative sample of Americans. The survey is designed to provide analysts with the data they need to support policy-relevant research on health care expenses, utilization, insurance coverage, and access in the United States and to provide policymakers with the results and data they need to make informed decisions. Objectives:This article summarizes the capacity of this broad-based and publicly available information resource to support research efforts directed towards achieving a better understanding of the dynamics of American healthcare and to better characterize its current state. Methods:The MEPS comprises a nationally representative sample of the civilian noninstitutionalized population in the United States, and collects comprehensive data on individuals and their health care experiences over a span of 2 years. Household survey data are collected by means of computer-assisted personal interviews, and those data are supplemented by information collected directly from the medical providers used by survey participants. Insurance data are collected both from households and through a separate state and nationally representative survey of business establishments, which collects information on health insurance provided by United States employers. Results:The MEPS has been used extensively in scientific publications and published reports, as well as by the Federal and state governments to examine the delivery and financing of healthcare in the United States. Conclusions:The analytical findings generated by the MEPS are key inputs to facilitate the development, implementation, and evaluation of policies and practices addressing health care in the United States and its related costs. Recent efforts to reconcile MEPS and the National Health Expenditure Accounts have the potential to provide an even more accurate and powerful data tool for research and policy analysis.

A Guide to Comparing Health Care Expenditures in the 1996 MEPS to the 1987 NMES

Background:Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36. Objectives:We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample. Methods:We used data from the 2000–2001 panel of the Medical Expenditure Panel Study. Respondents (n = 5542) completed the SF-12 in a questionnaire. Interviews obtained data on demographics and selected chronic conditions. Data on expenditures incurred subsequent to the interview were obtained in part from provider records. We examined different regression model specifications and compared different statistical estimation techniques. Results:Adding the SF-12 to a regression model improved the prediction of subsequent medical expenditures. In a model with only age and gender, adding the SF-12 increased R2 from 0.06 to 0.13. The coefficients for the Physical Component Summary (PCS) and the Mental Component Summary (MCS) of the SF-12 for this model were −0.045 (P < 0.01) and −0.012 (P < 0.01), respectively. In a model including demographic characteristics, chronic conditions, and previous expenditures, adding the SF-12 increased the R2 from 0.26 to 0.29. The coefficients for the PCS and the MCS for this model were −0.025 (P < 0.001) and −0.005 (P = 0.15), respectively. A single general health status question performed almost as well as the full SF-12. Models estimated using ordinary least squares had undesirable properties. In terms of R2, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function. Conclusions:Information on self-reported health status is useful in predicting medical expenditures. The extent to which the SF-12 adds predictive power over a comprehensive array of diagnostic data remains to be examined.

Fee-For-Service, While Much Maligned, Remains The Dominant Payment Method For Physician Visits

The goal of this study is to expand prior analyses by presenting current state‐level estimates of the costs of obesity in total and separately for Medicare and Medicaid. Quantifying current Medicare and Medicaid expenditures attributable to obesity is important because high public sector costs of obesity have been a primary motivation for publicly funded obesity prevention efforts at the state level. We also present estimates of the obesity‐attributable fraction (OAF) of total, Medicare, and Medicaid expenditures and the percentage of total obesity costs within each state that is funded by the public sector. We used the 2006 Medical Expenditure Panel Survey, nationally representative data that include information on obesity and medical expenditures, to generate an equation that predicts annual medical expenditures as a function of obesity status. We used the 2006 Behavioral Risk Factor Surveillance System, state representative data, and the equation generated from the national model to predict state (and payer within state) expenditures and the fraction of expenditures attributable to obesity for each state. Across states, annual medical expenditures would be between 6.7 and 10.7% lower in the absence of obesity. Between 22% (Virginia) and 55% (Rhode Island) of the state‐level costs of obesity are financed by the public sector via Medicare and Medicaid. The high costs of obesity at the state level emphasize the need to prevent and control obesity as a way to manage state medical costs.

Paying physicians by capitation: is the past now prologue?

Introduction:Accurate survey data on medical conditions are critical for health care researchers. Although medical condition data are complex and are subject to reporting error, little information exists on the quality of household reported condition data. Methods:We used pooled data from 4 years (2002–2005) of the Medical Expenditure Panel Survey (MEPS) to estimate the extent to which household respondents may underreport 23 types of medical conditions. The medical expenditure panel survey is a nationally representative annual survey of approximately 15,000 households which collects medical condition information in 2 separate components—the Household Component (HC) and the Medical Provider Component (MPC). We computed sensitivity rates based on linked HC and MPC data under the assumption that if collection of medical conditions from household respondents was complete, then the conditions reported in the MPC would also be reported in the HC. Results:Sensitivity rates ranged from a high of 93.8% to a low of 37.4% and were 75% or higher for 10 of the 23 conditions analyzed. The overall sensitivity rate for the 23 conditions combined was 74%. Conclusions:Household reports tended to be more accurate for conditions that are highly salient, cause pain, require hospitalization, require ongoing treatment, have specific recognizable treatment, alter lifestyle, and/or affect daily life (eg, pregnancy, diabetes, and kidney stones). In addition, reporting generally was better when conditions are classified in broader categories rather than in more detail.

Has the increase in HMO enrollment within the Medicaid population changed the pattern of health service use and expenditures

OBJECTIVE To compare the ability of different models to predict prospectively whether someone will incur high medical expenditures. DATA SOURCE Using nationally representative data from the Medical Expenditure Panel Survey (MEPS), prediction models were developed using cohorts initiated in 1996-1999 (N=52,918), and validated using cohorts initiated in 2000-2003 (N=61,155). STUDY DESIGN We estimated logistic regression models to predict being in the upper expenditure decile in Year 2 of a cohort, based on data from Year 1. We compared a summary risk score based on diagnostic cost group (DCG) prospective risk scores to a count of chronic conditions and indicators for 10 specific high-prevalence chronic conditions. We examined whether self-rated health and functional limitations enhanced prediction, controlling for clinical conditions. Models were evaluated using the Bayesian information criterion and the c-statistic. PRINCIPAL FINDINGS Medical condition information substantially improved prediction of high expenditures beyond gender and age, with the DCG risk score providing the greatest improvement in prediction. The count of chronic conditions, self-reported health status, and functional limitations were significantly associated with future high expenditures, controlling for DCG score. A model including these variables had good discrimination (c=0.836). CONCLUSIONS The number of chronic conditions merits consideration in future efforts to develop expenditure prediction models. While significant, self-rated health and indicators of functioning improved prediction only slightly.