HRS Bibliography

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Burkhauser RV, Daly MC. The Potential Impact on the Employment of People with Disabilities. In: West J, ed. Implementing the Americans with Disabilities Act. Implementing the Americans with Disabilities Act. Cambridge, MA: Blackwell Publishers; 1996:153-192.
Raykov T, Gorelick PB, Zajacova A, Marcoulides GA. On the potential of discrete time survival analysis using latent variable modeling: An application to the study of the vascular depression hypothesis. Structural Equation Modeling: A Multidisciplinary Journal. 2017:1 - 10. doi:10.1080/10705511.2017.1315305.
Dougherty DD. Potential to Privately Pay for Long-Term Care Services. 2000.
Gawronski KAB, Kim ES, Miller LE. Potentially traumatic events and serious life stressors are prospectively associated with frequency of doctor visits and overnight hospital visits. Journal of Psychosomatic Research. 2014;77(2):90-96. doi:10.1016/j.jpsychores.2014.05.009.
Chopik WJ, Wimer C, Betson DM, Manfield L. Poverty among the aged population: The role of out-of-pocket medical expenditures and annuitized assets in supplemental poverty measure estimates. 2018:47-75.
Bernstein SFae, Rehkopf DH, Tuljapurkar S, Horvitz CC. Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model. Komarova NL, ed. PLOS ONE. 2018;13(5):e0195734. doi:10.1371/journal.pone.0195734.
Poverty report links wealth to life expectancy. Prince George Citizen. https://proxy.lib.umich.edu/login?url=https://search.proquest.com/docview/2292028778?accountid=14667. Published 2019.PDF icon ProQuestDocuments-2019-09-30.pdf (208.67 KB)
Lee Y. Poverty Transitions for the Elderly. 2009:Ph.D.
Bronshtein G, Scott J, Shoven J, Slavov S. The Power of Working Longer. Cambridge, MA: National Bureau of Economic Research; 2018. doi:10.3386/w24226.
Barcelo H, Faul JD, Crimmins EM, Thyagarajan B. A Practical Cryopreservation and Staining Protocol for Immunophenotyping in Population Studies. Current Protocols in Cytometry. 2018;84(1):e35. doi:10.1002/cpcy.35.
http://www.ncbi.nlm.nih.gov/pubmed/30040214?dopt=Abstract
Lusardi A. Precautionary Saving and the Accumulation of Wealth. Dartmouth College; 2000.
Yilmazer T, Scharff RL. Precautionary Savings Against Health Risks: Evidence From the Health and Retirement Study. Research on Aging. 2013;36(2):180-206. doi:10.1177/0164027512473487.
Barsky R, Bound J, Charles KK, Lupton JP. A Precise Characterization of The Black-White Wealth Gap. University of Michigan; 2001.
Giustinelli P, Manski CF, Molinari F. Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia. National Bureau of Economic Research Working Paper Series. 2019;No. 26125. doi:10.3386/w26125.PDF icon w26125.pdf (535.1 KB)
Bobo JKay, Greek AA, Klepinger DH, Herting JR. Predicting 10-year alcohol use trajectories among men age 50 years and older. Am J Geriatr Psychiatry. 2013;21(2):204. doi:10.1097/JGP.0b013e3182423b4b.
Cruz M, Covinsky K, Widera EW, Stijacic-Cenzer I, Lee SJ. Predicting 10-year mortality for older adults. JAMA. 2013;309(9):874-6. doi:10.1001/jama.2013.1184.
http://www.ncbi.nlm.nih.gov/pubmed/23462780?dopt=Abstract
Boveda I, Metz AJ. Predicting End-of-Career Transitions for Baby Boomers Nearing Retirement Age. The Career Development Quarterly. 2016;64(2):153 - 168. doi:10.1002/cdq.2016.64.issue-210.1002/cdq.12048.
Banaszak-Holl J, A. Fendrick M, Foster NL, et al. Predicting Nursing Home Admission: Estimates from a seven-year follow-up of a nationally representative sample of older Americans. Alzheimer Disease and Associated Disorders. 2004;18(2):83-89.
Waddell EL, Jacobs-Lawson JM. Predicting positive well-being in older men and women. Int J Aging Hum Dev. 2010;70(3):181-97. doi:10.2190/AG.70.3.a.
http://www.ncbi.nlm.nih.gov/pubmed/20503804?dopt=Abstract
Blue L, Gill L, Faul JD, Bradway K, Stapleton DC. Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures: Evidence From the Health and Retirement Study. Journal of Aging and Health. 2019;31(4):555-579. doi:10.1177/0898264317737893.
http://www.ncbi.nlm.nih.gov/pubmed/29254420?dopt=Abstract
Geiger JR, Wilks SE, Livermore MM. Predicting SNAP Participation in Older Adults: Do Age Categorizations Matter?. Educational Gerontology. 2014;40(12):932-946. doi:10.1080/03601277.2014.912837.
Choi N, Bohman TM. Predicting the Changes in Depressive Symptomatology in Later Life: How much do changes in health status, marital and caregiving status, work and volunteering, and health-related behaviors contribute?. Journal of Aging and Health. 2007;19(1):152-77.
Islam MA, Chowdhury RI. Prediction of disease status: A regressive model approach for repeated measures. Statistical Methodology. 2010;7(5):520-540.
Chowdhury RI, Islam MA. Prediction of Disease Status: Transition Model Approach for Repeated Measures. Pakistan Journal of Statistics . 2014;30(2):181-196.PDF icon PAKJSTAT.pdf (403.25 KB)
Elder TE. The predictive validity of subjective mortality expectations: evidence from the health and retirement study. Demography. 2013;50(2):569. doi:10.1007/s13524-012-0164-2.