HRS Bibliography

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Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.
Fitzpatrick MD, Moore T. The Mortality Effects of Retirement: Evidence from Social Security Eligibility at Age 62. Cambridge, MA: National Bureau of Economic Research; 2017. doi:10.3386/w24127.
Reese PP, Bloom RD, Feldman HI, et al. Mortality and cardiovascular disease among older live kidney donors. Am J Transplant. 2014;14(8):1853-61. doi:10.1111/ajt.12822.
http://www.ncbi.nlm.nih.gov/pubmed/25039276?dopt=Abstract
Spector AL, Quinn KG, Wang I, Gliedt JA, Fillingim RB, Cruz-Almeida Y. More Problems, More Pain: The Role of Chronic Life Stressors and Racial/Ethnic Identity on Chronic Pain Among Middle-Aged and Older Adults in the United States. Chronic Stress (Thousand Oaks, Calif.). 2023;7:24705470231208281. doi:10.1177/24705470231208281.
Frostenson S. More Americans say they're in pain. It’s a fascinating and disturbing medical mystery.
Mayer A, Geiser C, Infurna FJ, Fiege C. Modelling and predicting complex patterns of change using growth component models: An application to depression trajectories in cancer patients. European Journal of Developmental Psychology. 2013;10(1):40-59. doi:10.1080/17405629.2012.732721.
Toder E, Thompson LH, Favreault M, et al. Modeling Income in the Near Term: Revised Projections of Retirement Income Through 2020 for the 1931-1960 Birth Cohorts. Washington, D.C.: The Urban Institute; 2002.
Favreault M, Smith KE. Modeling Income in the Near Term 8 and 2014. Urban Institute; 2019.
Hatfield LA, Favreault M, McGuire TG, Chernew ME. Modeling Health Care Spending Growth of Older Adults. Health Services Research. 2018;53:138-155. doi:10.1111/1475-6773.12640.
Faul J, Domingue B, Stenhaug B, West BT, Langa KM, Weir DR. MODE EFFECTS ON COGNITIVE FUNCTIONING ASSESSMENTS IN THE HEALTH AND RETIREMENT STUDY. Innovation in Aging. 2022;6(Suppl 1):163. doi:10.1093/geroni/igac059.652.
Domingue BW, McCammon R, West BT, Langa KM, Weir DR, Faul J. The mode effect of web-based surveying on the 2018 HRS measure of cognitive functioning. J Gerontol B Psychol Sci Soc Sci. 2023. doi:10.1093/geronb/gbad068.
B. Bernheim D, Forni L, Gokhale J, Kotlikoff LJ. The Mismatch Between Life Insurance Holdings and Financial Vulnerabilities: Evidence from the Health and Retirement Study. American Economic Review. 2003;93(1):354-365. doi:10.1257/000282803321455340.
Flores A. A mindfulness-based stress reduction program for lesbian, gay, bisexual, transgender older adults: A grant proposal. Social Work. 2016;M.S.W.:71.
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.
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.