Literature DB >> 12007164

Do Medicare HMOs still reduce health services use after controlling for selection bias?

Michelle M Mello1, Sally C Stearns, Edward C Norton.   

Abstract

This study models the relationship between Medicare beneficiary decisions to join Medicare HMOs and subsequent health services utilization. The relationship between health plan choice and utilization is thought to be endogenous because of favorable selection into HMOs. Previous studies found significantly lower inpatient utilization among Medicare HMO enrollees than among nonenrollees, but lacked strong controls for selection bias. Thus, a firm conclusion could not be drawn as to whether the observed differences were attributable to the HMO practice setting or to baseline differences in the illness profiles of the two groups studied. The present study uses simultaneous equations methods, including discrete factor estimation, to test the effect of Medicare HMOs on utilization when strong controls for selection bias are imposed. The model was run on a panel of 1993-1996 data from the Medicare Current Beneficiary Survey, supplemented with linked data on Medicare HMO characteristics and area supply characteristics. The study found that even when favorable selection is controlled for, Medicare HMOs significantly reduce both the probability of hospitalization and the number of inpatient days used by those who are hospitalized. Medicare HMOs do not, however, appear to reduce the use of physician services. Copyright 2002 John Wiley & Sons, Ltd.

Mesh:

Year:  2002        PMID: 12007164     DOI: 10.1002/hec.664

Source DB:  PubMed          Journal:  Health Econ        ISSN: 1057-9230            Impact factor:   3.046


  13 in total

1.  Understanding biased selection in Medicare HMOs.

Authors:  Michelle M Mello; Sally C Stearns; Edward C Norton; Thomas C Ricketts
Journal:  Health Serv Res       Date:  2003-06       Impact factor: 3.402

2.  Using global ratings of health plans to improve the quality of health care.

Authors:  Jacob Glazer; Thomas G McGuire; Zhun Cao; Alan Zaslavsky
Journal:  J Health Econ       Date:  2008-05-14       Impact factor: 3.883

3.  Selection bias and utilization of the dual eligibles in Medicare and Medicaid HMOs.

Authors:  Hui Zhang; Robert L Kane; Bryan Dowd; Roger Feldman
Journal:  Health Serv Res       Date:  2008-05-13       Impact factor: 3.402

4.  Association of Medicare Part D medication out-of-pocket costs with utilization of statin medications.

Authors:  Pinar Karaca-Mandic; Tami Swenson; Jean M Abraham; Robert L Kane
Journal:  Health Serv Res       Date:  2012-12-26       Impact factor: 3.402

5.  Medicare managed care plan performance: a comparison across hospitalization types.

Authors:  Jayasree Basu; Lee Rivers Mobley
Journal:  Medicare Medicaid Res Rev       Date:  2012-01-15

Review 6.  Methodological issues in using multiple years of the Medicare current beneficiary survey.

Authors:  Becky A Briesacher; Jennifer Tjia; Chyke A Doubeni; Yong Chen; Sowmya R Rao
Journal:  Medicare Medicaid Res Rev       Date:  2012-02-08

7.  Do Medicare Advantage enrollees tend to be admitted to hospitals with better or worse outcomes compared with fee-for-service enrollees?

Authors:  Bernard Friedman; H Joanna Jiang
Journal:  Int J Health Care Finance Econ       Date:  2010-02-06

8.  Screening for a Chronic Disease: A Multiple Stage Duration Model with Partial Observability.

Authors:  Thomas A Mroz; Gabriel Picone; Frank Sloan; Arseniy P Yashkin
Journal:  Int Econ Rev (Philadelphia)       Date:  2016-08-09

9.  The effect of HMOs on the inpatient utilization of medicare beneficiaries.

Authors:  Nasreen Dhanani; June F O'Leary; Emmett Keeler; Anil Bamezai; Glenn Melnick
Journal:  Health Serv Res       Date:  2004-10       Impact factor: 3.402

10.  How does drug treatment for diabetes compare between Medicare Advantage prescription drug plans (MAPDs) and stand-alone prescription drug plans (PDPs)?

Authors:  Mujde Z Erten; Bruce Stuart; Amy J Davidoff; J Samantha Shoemaker; Lynda Bryant-Comstock; Rahul Shenolikar
Journal:  Health Serv Res       Date:  2012-12-03       Impact factor: 3.402

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