Factors associated with sharing e-mail information and mental health survey participation in large population cohorts

International Journal of Epidemiology

Volumn 49, Issue 2, 2021, Pages 410-421

  Adams, Mark J ^a   David Hill, W ^a   Howard, David M ^a,b   Dashti, Hassan S ^c   Davis, Katrina A S ^b,d,e   Campbell, Archie ^a,f   Clarke, Toni Kim ^a   Deary, Ian J ^a   Hayward, Caroline ^a   Porteous, David ^a   Hotopf, Matthew ^b,d,e   McIntosh, Andrew M ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d SOUTH LONDON AND MAUDSLEY NHS FOUNDATION TRUST   (United Kingdom)

^e GSTFT and KCL   (United Kingdom)

^f UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Cohort studies; Follow up studies; Generation Scotland; Genome wide association study; Mental health; Partners Biobank; Selection bias; UK Biobank

Indexed keywords

COHORT ANALYSIS; DATABASE; EDUCATIONAL ATTAINMENT; GENETIC ALGORITHM; GENETIC ANALYSIS; MENTAL HEALTH; PHENOTYPE; PUBLIC HEALTH; SOCIAL PARTICIPATION;

ADULT; ARTICLE; BIOBANK; COHORT ANALYSIS; CONTROLLED STUDY; DISTRESS SYNDROME; E-MAIL; EDUCATIONAL STATUS; FEMALE; FOLLOW UP; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENE MAPPING; GENETIC ASSOCIATION; GENETIC RISK SCORE; GENOME-WIDE ASSOCIATION STUDY; HEALTH SURVEY; HUMAN; IDENTIFIABLE INFORMATION; MAJOR CLINICAL STUDY; MALE; MENTAL HEALTH; PHENOTYPE; POPULATION RESEARCH; PRIORITY JOURNAL; SCHIZOPHRENIA; SCOTLAND; SELECTION BIAS; SINGLE NUCLEOTIDE POLYMORPHISM; UNITED KINGDOM;

SCOTLAND; UNITED KINGDOM;

COHORT STUDIES; ELECTRONIC MAIL; GENOME-WIDE ASSOCIATION STUDY; HEALTH SURVEYS; HUMANS; MENTAL HEALTH; PHENOTYPE; UNITED KINGDOM;

EID: 85085904128     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/IJE/DYZ134     Document Type: Article

References (23)

1. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004;15:615-25.
2. Munafò MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol 2018;47:226-35.
3. Lamers F, Hoogendoorn AW, Smit JH et al. Sociodemographic and psychiatric determinants of attrition in the Netherlands Study of Depression and Anxiety (NESDA). Compr Psychiatry 2012;53:63-70.
4. Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York, NY: Cambridge University Press, 2007.
5. Robins JM, Hernán MÁ, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11:550-60.
6. Martin J, Tilling K, Hubbard L et al. Association of genetic risk for schizophrenia with nonparticipation over time in a population-based cohort study. Am J Epidemiol 2016;183: 1149-58.
7. Taylor AE, Jones HJ, Sallis H et al. Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 2018;47:1207-16.
8. Rothman KJ, Gallacher JE, Hatch EE. Why representativeness should be avoided. Int J Epidemiol 2013;42:1012-14.
9. Davis KAS, Coleman JRI, Adams M et al. Mental health in UK Biobank: Development, implementation and results from an online questionnaire completed by 157 366 participants. BJPsych Open 2018;4:83-90.
10. Navrady LB, Wolters MK, MacIntyre DJ et al. Cohort Profile: Stratifying Resilience and Depression Longitudinally (STRADL): a questionnaire follow-up of Generation Scotland: Scottish Family Health Study (GS: SFHS). Int J Epidemiol 2018;47:13-14g.
11. Karlson E, Boutin N, Hoffnagle A, Allen N. Building the Partners HealthCare Biobank at Partners Personalized Medicine: informed consent, return of research results, recruitment lessons and operational considerations. J Pers Med 2016;6:2.
12. Smith BH, Campbell H, Blackwood D et al. Generation Scotland: The Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med Genet 2006;7:74.
13. Smith BH, Campbell A, Linksted P et al. Cohort Profile: Generation Scotland: Scottish Family Health Study (GS: SFHS). The study, its participants and their potential for genetic research on health and illness. Int J Epidemiol 2013;42:689-700.
14. Dashti HS, Redline S, Saxena R. Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank. Sleep 2018;42:1-10.
15. R Development Core Team. R: A Language and Environment for Statistical Computing. 3.5. Vienna: R Foundation for Statistical Computing, 2018.
16. Devlin B, Roeder K. Genomic control for association studies. Biometrics 1999;55:997-1004.
17. Bulik-Sullivan BK, Loh P-R, Finucane HK et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015;47:291-95.
18. Johnson JL, Abecasis GR. GAS Power Calculator: Web-based power calculator for genetic association studies. bioRxiv 2017. doi: 10.1101/164343.
19. Zheng J, Erzurumluoglu AM, Elsworth BL et al. LD Hub: A centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 2017;33:272-79.
20. Fry A, Littlejohns TJ, Sudlow C et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol 2017;186:1026-34.
21. Coon KD, Myers AJ, Craig DW et al. A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease. J Clin Psychiatry 2007;68:613-18.
22. Pilkis SJ, Claus TH, Kurland IJ, Lange AJ. 6-phosphofructo-2-ki-nase/fructose-2, 6-bisphosphatase: A metabolic signaling enzyme. Annu Rev Biochem 1995;64:799-835.
23. Verma T, Pearl J. Equivalence and synthesis of causal models. In: Schachter R, Levitt TS, Kanal LN (eds). Uncertainty in Artificial Systems. Amsterdam: Elsevier, 1991.