HRS Bibliography

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Bahrampour T. Middle-income seniors risk falling through cracks in housing market. The Washington Post. https://www.washingtonpost.com/dc-md-va/2019/05/28/middle-income-seniors-risk-falling-through-cracks-housing-market/?noredirect=on&utm_term=.91fda3cf4f5b. Published 2019.
B. Huber R. Middle-aged Americans report more pain than the elderly. Research News.
Laditka SB, Laditka JN, Jagger C. Microsimulation of Health Expectancies, Life Course Health, and Health Policy Outcomes. In: Jagger C, Crimmins EM, Saito Y, Yokota RTiene De C, Van Oyen H, Robine J-M, eds. International Handbooks of Population: International Handbook of Health Expectancies. Vol. 9. International Handbooks of Population: International Handbook of Health Expectancies. Basel: Springer International Publishing; 2020:129–138. doi:10.1007/978-3-030-37668-0_9.
Cahill KE, Giandrea MD, Quinn JF. A Micro-Level Analysis of Recent Increases in Labor Force Participation Among Older Workers. Boston: Center for Retirement Research at Boston College; 2008.
Quinn JF, Burkhauser RV, Cahill KE, Weathers, II RR. The Microeconomics of the Retirement Decision in the United States.; 1998.
Reed E. The Microeconomic Effect of Longevity on Savings: Evidence from the Health and Retirement Study. 2005.
Burkhauser RV, Smeeding TM. Microdata Panel Data and Public Policy: National and Cross-National Perspectives. Syracuse, NY: Syracuse University; 2000.
Benítez-Silva H. Micro Determinants of Labor Force Status Among Older Americans. SUNY-Stony Brook; 2000.
Qiao Z, Powell J. MHC-Dependent Mate Selection within 872 Spousal Pairs of European Ancestry from the Health and Retirement Study. Genes. 2018;9(2):53. doi:10.3390/genes9010053.
Mejia-Arango S, Nevarez R, Michaels-Obregon A, et al. The Mexican Cognitive Aging Ancillary Study (Mex-Cog): Study Design and Methods. Archives of Gerontology and Geriatrics. 2020;91. doi:https://doi.org/10.1016/j.archger.2020.104210.
Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. Biostatistics. 2018;PhD:110.
United States Congressional Office. Methods for Analysis of the Financing and Use of Long-Term Services and Supports. Washington, DC; 2013.
Briceño E, Mehdipanah R, Gonzales X, et al. Methods and Early Recruitment of a Community-Based Study of Cognitive Impairment Among Mexican Americans and Non-Hispanic Whites: The BASIC-Cognitive Study. Journal of Alzheimer's Disease. 2020;73(1):185-196. doi:10.3233/JAD-190761.
Hurd MD, Rohwedder S. Methodological Innovations in Collecting Spending Data: The HRS Consumption and Activities Mail Survey. Fisc Stud. 2009;30(3-4):435-459. doi:10.1111/j.1475-5890.2009.00103.x.
http://www.ncbi.nlm.nih.gov/pubmed/21052480?dopt=Abstract
Banerjee J, Jain U, Khobragade PY, et al. Methodological considerations in designing and implementing the harmonized diagnostic assessment of dementia for longitudinal aging study in India (LASI-DAD). Biodemography and Social Biology. 2020;65(3):189-213. doi:10.1080/19485565.2020.1730156.
Lee S, Smith J. Methodological Aspects of Subjective Life Expectancy: Effects of Culture-Specific Reporting Heterogeneity Among Older Adults in the United States. The Journals of Gerontology: Series B. 2016;71(3):558-568. doi:10.1093/geronb/gbv048.
Pare G, Mao S, Deng WQ. A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics. Sci Rep. 2016;6:27644. doi:10.1038/srep27644.
http://www.ncbi.nlm.nih.gov/pubmed/27273519?dopt=Abstract
Tweedy MP. Metabolic Syndrome and Psychosocial Factors. 2009.
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
Yengo L, Sidorenko J, Kemper KE, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Human Molecular Genetics. 2018;27(20):3641-3649. doi:10.1093/hmg/ddy271.
http://www.ncbi.nlm.nih.gov/pubmed/30124842?dopt=Abstract
Yengo L, Sidorenko J, Kemper KE, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Human Molecular Genetics. 2018;27(20):3641-3649. doi:10.1093/hmg/ddy271.
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.
Fisher GG, Stachowski A, Infurna FJ, Faul JD, Grosch J, Tetrick LE. Mental work demands, retirement, and longitudinal trajectories of cognitive functioning. J Occup Health Psychol. 2014;19(2):231-42. doi:10.1037/a0035724.
http://www.ncbi.nlm.nih.gov/pubmed/24635733?dopt=Abstract
Bingley P, Martinello A. Mental retirement and schooling. European Economic Review. 2013;63(October 2013):292-298. doi:http://dx.doi.org/10.1016/j.euroecorev.2013.01.004.
Clouston SAP, Denier N. Mental retirement and health selection: Analyses from the U.S. Health and Retirement Study. Social Science & Medicine. 2017;178:78-86. doi:10.1016/j.socscimed.2017.01.019.