HRS Bibliography

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F. Juster T. On the Measurement of Expectations, Uncertainty, and Preferences. The Journals of Gerontology: Social Sciences. 1997;52B(5):S237-9.
Putnam M, Molton IR, Truitt AR, Smith AE, Jensen MP. Measures of aging with disability in U.S. secondary data sets: Results of a scoping review. Disability and Health Journal. 2016;9(1):5-10. doi:10.1016/j.dhjo.2015.07.002.
MacEwan JP, Gill TM, Johnson K, et al. Measuring Sarcopenia Severity in Older Adults and the Value of Effective Interventions. The journal of nutrition, health & aging. 2018. doi:10.1007/s12603-018-1104-7.
An C-B, Jeon S-H. Measuring the Optimal Income Replacement Rate: A Panel Data Analysis. Sungkyunkwan University, Korea; Dept. of Economics; 2003.
Yoon D, Gallo H, Gassoumis ZD, Joo S. The Mediating Role of Sense of Control in the Associations Between Remote Contacts and Loneliness Among Older Adults. Research on Aging. 2024;46(2):167-175. doi:10.1177/01640275231206484.
Miller S, Johnson N, Wherry LR. MEDICAID AND MORTALITY: NEW EVIDENCE FROM LINKED SURVEY AND ADMINISTRATIVE DATA. Quarterly Journal of Economics. 2021;136(3):1783–1829. doi:10.1093/qje/qjab004.
De Nardi M, French E, Jones JBailey. Medicaid Insurance in Old Age. Ann Arbor, MI: Michigan Retirement and Disability Research Center, University of Michigan; 2012.
Jones JBailey, De Nardi M, French E, McGee R, Rodgers R. Medical Spending, Bequests, and Asset Dynamics Around the Time of Death. Cambridge, MA: National Bureau of Economic Research; 2020. doi:10.3386/w26879.
Johnson RW. Medicare and Retirement Decisions. The Urban Institute; 2001.
Johnson RW, Moon M, Davidoff AJ. A Medicare Buy In for the Near Elderly: Design Issues and Potential Effects on Coverage. Washington, D.C.: Henry J. Kaiser Family Foundation; 2002.
Johnson RW. Medicare, Retirement Costs, and Labor Supply at Older Ages. Boston: Center for Retirement Research at Boston College; 2002.
W Deardorff J, Jing B, Growdon ME, et al. Medication misuse and overuse in community-dwelling persons with dementia. J Am Geriatr Soc. 2023. doi:10.1111/jgs.18463.
Rentería MArce, Briceño EM, Chen D, et al. Memory and language cognitive data harmonization across the United States and Mexico. Alzheimer's & Dementia (Amsterdam, Netherlands). 2023;15(3):e12478. doi:10.1002/dad2.12478.
Jajodia A, Borders A. Memory predicts changes in depressive symptoms in older adults: a bidirectional longitudinal analysis. J Gerontol B Psychol Sci Soc Sci. 2011;66(5):571-81. doi:10.1093/geronb/gbr035.
http://www.ncbi.nlm.nih.gov/pubmed/21742642?dopt=Abstract
Kong D, Lu P, Jiang D, Chan HYue Lai. Memory trajectories and disability among older couples: the mediating role of depressive symptoms. Journal of Applied Gerontology, Series B, Psychological Sciences and social sciences. Forthcoming. doi:10.1093/geronb/gbad163.
Bakulski KM, Fu M, Faul J, Jin Y, Ware EB. Mendelian randomization of smoking behavior on cognitive status among older Americans. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2020;16(S10):e041221. doi:10.1002/alz.041221.
Liu C, Kraja AT, Smith JA, Morrison AC, et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016;48(10):1162-70. doi:10.1038/ng.3660.
Deelen J, Evans DS, Arking DE, et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nature Communications. 2019;10(1):3669. doi:10.1038/s41467-019-11558-2.
http://www.ncbi.nlm.nih.gov/pubmed/31413261?dopt=Abstract
Deelen J, Evans DS, Arking DE, et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nature Communications. 2019;10(1):3669. doi:10.1038/s41467-019-11558-2.
http://www.ncbi.nlm.nih.gov/pubmed/31413261?dopt=Abstract
Deelen J, Evans DS, Arking DE, et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nature Communications. 2019;10(1):3669. doi:10.1038/s41467-019-11558-2.
http://www.ncbi.nlm.nih.gov/pubmed/31413261?dopt=Abstract
A Erzurumluoglu M, Liu M, Jackson VE, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Molecular Psychiatry. 2020;25(10):2392-2409. doi:10.1038/s41380-018-0313-0.
A Erzurumluoglu M, Liu M, Jackson VE, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Molecular Psychiatry. 2020;25(10):2392-2409. doi:10.1038/s41380-018-0313-0.
A Erzurumluoglu M, Liu M, Jackson VE, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Molecular Psychiatry. 2020;25(10):2392-2409. doi:10.1038/s41380-018-0313-0.
A Erzurumluoglu M, Liu M, Jackson VE, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Molecular Psychiatry. 2020;25(10):2392-2409. doi:10.1038/s41380-018-0313-0.
A Erzurumluoglu M, Liu M, Jackson VE, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Molecular Psychiatry. 2020;25(10):2392-2409. doi:10.1038/s41380-018-0313-0.