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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2012

Open Access 01-12-2012 | Research article

The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms

Authors: Arnulf Langhammer, Steinar Krokstad, Pål Romundstad, Jon Heggland, Jostein Holmen

Published in: BMC Medical Research Methodology | Issue 1/2012

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Abstract

Background

Population based studies are important for prevalence, incidence and association studies, but their external validity might be threatened by decreasing participation rates. The 50 807 participants in the third survey of the HUNT Study (HUNT3, 2006-08), represented 54% of the invited, necessitating a nonparticipation study.

Methods

Questionnaire data from HUNT3 were compared with data collected from several sources: a short questionnaire to nonparticipants, anonymous data on specific diagnoses and prescribed medication extracted from randomly selected general practices, registry data from Statistics Norway on socioeconomic factors and mortality, and from the Norwegian Prescription Database on drug consumption.

Results

Participation rates for HUNT3 depended on age, sex and type of symptoms and diseases, but only small changes were found in the overall prevalence estimates when including data from 6922 nonparticipants. Among nonparticipants, the prevalences of cardiovascular diseases, diabetes mellitus and psychiatric disorders were higher both in nonparticipant data and data extracted from general practice, compared to that reported by participants, whilst the opposite pattern was found, at least among persons younger than 80 years, for urine incontinence, musculoskeletal pain and headache. Registry data showed that the nonparticipants had lower socioeconomic status and a higher mortality than participants.

Conclusion

Nonparticipants had lower socioeconomic status, higher mortality and showed higher prevalences of several chronic diseases, whilst opposite patterns were found for common problems like musculoskeletal pain, urine incontinence and headache. The impact on associations should be analyzed for each diagnosis, and data making such analyses possible are provided in the present paper.
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Literature
1.
Galea S, Tracy M: Participation rates in epidemiologic studies. Ann Epidemiol. 2007, 17 (9): 643-653. 10.1016/j.annepidem.2007.03.013.CrossRefPubMed
2.
Holmen J, Midthjell K, Forsen L, Skjerve K, Gorseth M, Oseland A: A health survey in Nord-Trondelag 1984-86. Participation and comparison of attendants and non-attendants. Tidsskr Nor Laegeforen. 1990, 110 (15): 1973-1977.PubMed
3.
4.
Krokstad S, Langhammer A, Hveem K, Holmen T, Midthjell K, Stene T, et al: Cohort profile: the HUNT Study, Norway. Int J Epidemiol. 2012, Aug 9. [Epub ahead of print]
5.
Knudsen AK, Hotopf M, Skogen JC, Overland S, Mykletun A: The health status of nonparticipants in a population-based health study: the Hordaland Health Study. Am J Epidemiol. 2010, 172 (11): 1306-1314. 10.1093/aje/kwq257.CrossRefPubMed
6.
Sogaard AJ, Selmer R, Bjertness E, Thelle D: The Oslo Health Study: the impact of self-selection in a large, population-based survey. Int J Equity Health. 2004, 3 (1): 3-10.1186/1475-9276-3-3.CrossRefPubMedPubMedCentral
7.
Langhammer A, Johnsen R, Holmen J, Gulsvik A, Bjermer L: Cigarette smoking gives more respiratory symptoms among women than among men. The Nord-Trondelag Health Study (HUNT). J Epidemiol Community Health. 2000, 54 (12): 917-922. 10.1136/jech.54.12.917.CrossRefPubMedPubMedCentral
8.
9.
10.
11.
Sogaard AJ, Bjelland E, Tell GS, Roysamb E: A comparison of the CONOR Mental Health Index to the HSCL-10 and HADS. Measuring mental health status in the Oslo Health Study and the Nord-Trøndelag Health Study. Norsk Epidemiol. 2003, 13 (2): 279-284.
12.
Zigmond AS, Snaith RP: The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983, 6: 361-370.CrossRef
13.
Bjelland I, Dahl AA, Haug TT, Neckelmann D: The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res. 2002, 52 (2): 69-77. 10.1016/S0022-3999(01)00296-3.CrossRefPubMed
14.
Jacobsen BK, Eggen AE, Mathiesen EB, Wilsgaard T, Njolstad I: Cohort profile: the Tromso Study. Int J Epidemiol. 2012 Aug, 41 (4): 961-7. 10.1093/ije/dyr049.CrossRefPubMed
15.
Tambs K, Ronning T, Prescott CA, Kendler KS, Reichborn-Kjennerud T, Torgersen S, et al: The Norwegian Institute of Public Health Twin Study of Mental Health: examining recruitment and attrition bias. Twin Res Hum Genet. 2009, 12 (2): 158-168. 10.1375/twin.12.2.158.CrossRefPubMedPubMedCentral
16.
17.
18.
Davis TC, Holcombe RF, Berkel HJ, Pramanik S, Divers SG: Informed consent for clinical trials: a comparative study of standard versus simplified forms. J Natl Cancer Inst. 1998, 90 (9): 668-674. 10.1093/jnci/90.9.668.CrossRefPubMed
19.
Ross S, Grant A, Counsell C, Gillespie W, Russell I, Prescott R: Barriers to participation in randomised controlled trials: a systematic review. J Clin Epidemiol. 1999, 52 (12): 1143-1156. 10.1016/S0895-4356(99)00141-9.CrossRefPubMed
20.
Asch DA, Jedrziewski MK, Christakis NA: Response rates to mail surveys published in medical journals. J Clin Epidemiol. 1997, 50 (10): 1129-1136. 10.1016/S0895-4356(97)00126-1.CrossRefPubMed
21.
Jacobsen BK, Thelle DS: The Tromso Heart Study: responders and non-responders to a health questionnaire, do they differ?. Scand J Soc Med. 1988, 16 (2): 101-104.PubMed
22.
Subar AF, Ziegler RG, Thompson FE, Johnson CC, Weissfeld JL, Reding D, et al: Is shorter always better? Relative importance of questionnaire length and cognitive ease on response rates and data quality for two dietary questionnaires. Am J Epidemiol. 2001, 153 (4): 404-409. 10.1093/aje/153.4.404.CrossRefPubMed
23.
Lund E, Dumeaux V, Braaten T, Hjartaker A, Engeset D, Skeie G, et al: Cohort profile: the Norwegian Women and Cancer Study–NOWAC–Kvinner og kreft. Int J Epidemiol. 2008, 37 (1): 36-41.CrossRefPubMed
24.
Olson SH: Reported participation in case–control studies: changes over time. Am J Epidemiol. 2001, 154 (6): 574-581. 10.1093/aje/154.6.574.CrossRefPubMed
25.
Jackson R, Chambless LE, Yang K, Byrne T, Watson R, Folsom A, et al: Differences between respondents and nonrespondents in a multicenter community-based study vary by gender ethnicity. The Atherosclerosis Risk in Communities (ARIC) study investigators. J Clin Epidemiol. 1996, 49 (12): 1441-1446. 10.1016/0895-4356(95)00047-X.CrossRefPubMed
26.
Korkeila K, Suominen S, Ahvenainen J, Ojanlatva A, Rautava P, Helenius H, et al: Non-response and related factors in a nation-wide health survey. Eur J Epidemiol. 2001, 17 (11): 991-999. 10.1023/A:1020016922473.CrossRefPubMed
27.
Launer LJ, Wind AW, Deeg DJ: Nonresponse pattern and bias in a community-based cross-sectional study of cognitive functioning among the elderly. Am J Epidemiol. 1994, 139 (8): 803-812.PubMed
28.
Van Loon AJ, Tijhuis M, Picavet HS, Surtees PG, Ormel J: Survey non-response in the Netherlands: effects on prevalence estimates and associations. Ann Epidemiol. 2003, 13 (2): 105-110. 10.1016/S1047-2797(02)00257-0.CrossRefPubMed
29.
Kreiger N, Nishri ED: The effect of nonresponse on estimation of relative risk in a case–control study. Ann Epidemiol. 1997, 7 (3): 194-199. 10.1016/S1047-2797(97)00013-6.CrossRefPubMed
30.
Shahar E, Folsom AR, Jackson R: The effect of nonresponse on prevalence estimates for a referent population: insights from a population-based cohort study. Atherosclerosis Risk in Communities (ARIC) study investigators. Ann Epidemiol. 1996, 6 (6): 498-506. 10.1016/S1047-2797(96)00104-4.CrossRefPubMed
31.
Eagan TM, Eide GE, Gulsvik A, Bakke PS: Nonresponse in a community cohort study: predictors and consequences for exposure-disease associations. J Clin Epidemiol. 2002, 55 (8): 775-781. 10.1016/S0895-4356(02)00431-6.CrossRefPubMed
32.
Hansen V, Jacobsen BK, Arnesen E: Prevalence of serious psychiatric morbidity in attenders and nonattenders to a health survey of a general population: the Tromso Health Study. Am J Epidemiol. 2001, 154 (10): 891-894. 10.1093/aje/154.10.891.CrossRefPubMed
33.
Haapea M, Miettunen J, Laara E, Joukamaa MI, Jarvelin MR, Isohanni MK, et al: Non-participation in a field survey with respect to psychiatric disorders. Scand J Public Health. 2008, 36 (7): 728-736. 10.1177/1403494808092250.CrossRefPubMed
34.
Stang A: Nonresponse research–an underdeveloped field in epidemiology. Eur J Epidemiol. 2003, 18 (10): 929-931.CrossRefPubMed
35.
Bisgard KM, Folsom AR, Hong CP, Sellers TA: Mortality and cancer rates in nonrespondents to a prospective study of older women: 5-year follow-up. Am J Epidemiol. 1994, 139 (10): 990-1000.PubMed
36.
Drivsholm T, Eplov LF, Davidsen M, Jorgensen T, Ibsen H, Hollnagel H, et al: Representativeness in population-based studies: a detailed description of non-response in a Danish cohort study. Scand J Public Health. 2006, 34 (6): 623-631. 10.1080/14034940600607616.CrossRefPubMed
37.
Hayes AJ, Clarke PM, Lung TW: Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations. Popul Health Metr. 2011, 9: 53-10.1186/1478-7954-9-53.CrossRefPubMedPubMedCentral
38.
Burg JA, Allred SL, Sapp JH: The potential for bias due to attrition in the national exposure registry: an examination of reasons for nonresponse, nonrespondent characteristics, and the response rate. Toxicol Ind Health. 1997, 13 (1): 1-13.PubMed
39.
Wild TC, Cunningham J, Adlaf E: Nonresponse in a follow-up to a representative telephone survey of adult drinkers. J Stud Alcohol. 2001, 62 (2): 257-261.CrossRefPubMed
40.
Lahkola A, Salminen T, Auvinen A: Selection bias due to differential participation in a case–control study of mobile phone use and brain tumors. Ann Epidemiol. 2005, 15 (5): 321-325. 10.1016/j.annepidem.2004.12.009.CrossRefPubMed
41.
Herr CE, Zur Nieden A, Jankofsky M, Stilianakis NI, Boedeker RH, Eikmann TF: Effects of bioaerosol polluted outdoor air on airways of residents: a cross sectional study. Occup Environ Med. 2003, 60 (5): 336-342. 10.1136/oem.60.5.336.CrossRefPubMedPubMedCentral
42.
Ferrie JE, Kivimaki M, Singh-Manoux A, Shortt A, Martikainen P, Head J, et al: Non-response to baseline, non-response to follow-up and mortality in the Whitehall II cohort. Int J Epidemiol. 2009, 38 (3): 831-837. 10.1093/ije/dyp153.CrossRefPubMedPubMedCentral
43.
Harald K, Salomaa V, Jousilahti P, Koskinen S, Vartiainen E: Non-participation and mortality in different socioeconomic groups: the FINRISK population surveys in 1972-92. J Epidemiol Community Health. 2007, 61 (5): 449-454. 10.1136/jech.2006.049908.CrossRefPubMedPubMedCentral
Metadata
Title
The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms
Authors
Arnulf Langhammer
Steinar Krokstad
Pål Romundstad
Jon Heggland
Jostein Holmen
Publication date
01-12-2012
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2012
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-12-143

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