Disability Forecasts and Future Medicare Costs
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| 245 | 0 | 0 | |a Disability Forecasts and Future Medicare Costs |h [Elektronische Daten] |c [Jayanta Bhattacharya, David M. Cutler, Dana P. Goldman, Michael D. Hurd, Geoffrey F. Joyce, Darius N. Lakdawalla, Constantijn W. A. Panis, Baoping Shang] |
| 520 | 3 | |a The traditional focus of disability research has been on the elderly, with good reason. Chronic disability is much more prevalent among the elderly, and it has a more direct impact on the demand for medical care. It is also important to understand trends in disability among the young, however, particularly if these trends diverge from those among the elderly. These trends could have serious implications for future health care spending because more disability at younger ages almost certainlytranslates into more disability among tomorrows elderly, and disability is a key predictor of health care spending.Using data from the Medicare Current Beneficiary Survey (MCBS) and the National Health Interview Study (NHIS), we forecast that per-capita Medicare costs will decline for the next fifteen to twenty years, in accordance with recent projections of declining disability among the elderly. By 2020, however, the trend reverses. Per-capita costs begin to rise due to growth in disability among the younger elderly. Total costs may well remain relatively flat until 2010 and then begin to rise because per-capita costs will cease to decline rapidly enough to offset the influx of new elderly people. Overall, cost forecasts for the elderly that incorporate information about disability among todays younger generations yield more pessimistic scenarios than those based solely on elderly data sets, and this information should be incorporated into official Medicare forecasts. | |
| 540 | |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston | ||
| 700 | 1 | |a Bhattacharya |D Jayanta |u Stanford University and NBER |4 aut | |
| 700 | 1 | |a Cutler |D David M. |u Harvard University and NBER |4 aut | |
| 700 | 1 | |a Goldman |D Dana P. |u RAND and NBER |4 aut | |
| 700 | 1 | |a Hurd |D Michael D. |u RAND and NBER |4 aut | |
| 700 | 1 | |a Joyce |D Geoffrey F. |u RAND |4 aut | |
| 700 | 1 | |a Lakdawalla |D Darius N. |u RAND and NBER |4 aut | |
| 700 | 1 | |a Panis |D Constantijn W. A. |u RAND |4 aut | |
| 700 | 1 | |a Shang |D Baoping |u RAND |4 aut | |
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| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Bhattacharya |D Jayanta |u Stanford University and NBER |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Cutler |D David M. |u Harvard University and NBER |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Goldman |D Dana P. |u RAND and NBER |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hurd |D Michael D. |u RAND and NBER |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Joyce |D Geoffrey F. |u RAND |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lakdawalla |D Darius N. |u RAND and NBER |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Panis |D Constantijn W. A. |u RAND |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Shang |D Baoping |u RAND |4 aut | ||
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