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The Pent-up Demand for Health Care of the Uninsured Near Elderly Approaching Age 65

The Pent-up Demand for Health Care of the Uninsured Near Elderly Approaching Age 65 . Li-Wu Chen, Ph.D. University of Nebraska Medical Center AcademyHealth 2004 Annual Meeting San Diego, June 6, 2004. ACKNOWLEDGMENT . Co-authors: Wanqing Zhang, M.Ed. Jane Meza, Ph.D.

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The Pent-up Demand for Health Care of the Uninsured Near Elderly Approaching Age 65

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  1. The Pent-up Demand for Health Care of the Uninsured Near Elderly Approaching Age 65 Li-Wu Chen, Ph.D. University of Nebraska Medical Center AcademyHealth 2004 Annual Meeting San Diego, June 6, 2004

  2. ACKNOWLEDGMENT Co-authors: • Wanqing Zhang, M.Ed. • Jane Meza, Ph.D. • Phani Tej Adidam, Ph.D. • Keith Mueller, Ph.D. • Louis Pol, Ph.D. • Dennis Shea, Ph.D. • Roslyn Fraser, MA Funded by The Economic Research Initiative for the Uninsured (ERIU) at the University of Michigan Special Thanks to Dr. Catherine McLaughlin

  3. The Uninsurance of the Near Elderly (Ages 55-64) • Uninsurance Rate: 12.9% in 2002 (CPS) • Highly Vulnerable to the Consequence of Uninsurance: --- more likely to report fair/poor health --- more likely to have health conditions --- more financially vulnerable

  4. Consequences of Being Uninsured for the Near Elderly Previous studies have shown: • less use of preventive care; • more severe once diagnosed with disease; • less use of therapeutic care. May use even less care: • Not only because of uninsurance; • But because of approaching Medicare eligibility (i.e., Pent-up Demand)  Increase financial burden on Medicare

  5. Pent-up Demand • Definition: Knowing that they will be covered by insurance at a near future time point, the uninsured postpone their health care use until they are insured. • If true, then empirical evidence would show that the uninsured near elderly --- reduce pre-Medicare utilization --- increase post-Medicare utilization

  6. Research Design • Hypothesis: Pent-up demand exists for the uninsured near elderly approaching 65. • Data: Health and Retirement Study (HRS) • Study Sample: Ages 63-67 (n=640) • Method: Use multivariate logistic regression (difference-in-difference) model to examine whether being uninsured (as opposed to insured) at 63 would increase the likelihood of having increased use of health care from 63-65 to 65-67. (covariates: sex, race, education, marital status, income, perceived health status, health conditions, ADLs, health behaviors).

  7. The Adjusted Odds Ratios of Having Increased Use for Being Uninsured (as opposed to being insured) At Age 63 * Significant at P<0.10 ** Significant at P<0.05 *** Significant at P<0.01

  8. Conclusions • Pent-up demand for physician care exists for the uninsured near elderly approaching age 65. • Pent-up demand for hospital inpatient care does not exist for the uninsured near elderly approaching age 65.

  9. The Magnitude of Catch-up Effect Further Analysis: Cross-sectional regression model of physician visits during post-Medicare period (i.e., ages 65-67) Results: The previously uninsured (at age 63)used about 30% more of physician visits than their previously insured counterparts (p<0.05) during the two years after Medicare enrollment (i.e., ages 65-67), after controlling for gender, race, education, marital status, income, health status, and health behavior at the baseline (i.e., age=65).

  10. Policy Implications (1) • Worsening health condition of the uninsured near elderly • Financial burden on Medicare program --- An estimated increase of expenditures by approximately $600 per (previously uninsured) beneficiary per year (if using $1,936 as the average annual expenditures in physician/supplier services per beneficiary aged 65-74; 1999, MCBS)

  11. Policy Implications (2) Estimating Potential Savings for Medicare Buy-in Proposals: • Example from other studies : A general buy-in without tax credit (under Clinton’s proposal) would induce a total of 176,600 people to enroll (11,600 out of them are uninsured), costing the program $14.3 million. (Sheils & Chen, 2001) • Using the estimate from our study: The potential saving through buy-in would be approximately $7 million ($600 x 11,600)

  12. Limitations Limitation: • Short study time frame • Point-in-time measure for the insurance status at age 63

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