Top

BMC Public Health

Published in:

Open Access 01-12-2018 | Research article

Information bias of social gradients in sickness absence: a comparison of self-report data in the Norwegian Mother and Child Cohort Study (MoBa) and data in national registries

Authors: Petter Kristensen, Karina Corbett, Ferdinand A. Mohn, Therese N. Hanvold, Ingrid S. Mehlum

Published in: BMC Public Health | Issue 1/2018

Abstract

Background

Measurement error in self-report questionnaires is a common source of bias in epidemiologic studies. The study aim was to assess information bias of the educational gradient in sickness absence among participants in the Norwegian Mother and Child Cohort Study (MoBa), comparing self-report data with national register data.

Methods

MoBa is a national prospective cohort study. The present study included 49,637 participants, born 1967–1976, who gave birth 2000–2009. The highest completed education level was recorded in categories and as educational years. Sickness absence was defined as one or more spell lasting more than 16 days between pregnancy weeks 13 and 30. We computed sickness absence risk in mid-pregnancy in strata of education level. Associations between completed educational years and sickness absence were estimated as risk differences in binomial regression and compared between self-report and register data. In additional analyses, we aimed to explain discrepancies between estimates from the two data sources.

Results

The overall registry-based sickness absence risk was 0.478 and decreased for increasingly higher education in a consistent fashion, yielding an additive risk difference in association with one additional education year of − 0.032 (95% confidence interval − 0.035 to − 0.030). The self-report risk was lower (0.307) with a corresponding risk difference of only − 0.013 (95% confidence interval − 0.015 to − 0.011). The main explanation of the lower risk difference in the self-report data was a tendency for mothers in low education categories to omit reporting sickness absence in the questionnaire.

Conclusions

A plausible explanation for the biased self-report association is complexity of the sickness absence question and a resulting educational gradient in non-response. As shown for sickness absence in mid-pregnancy in the present study, national registries could be a preferred alternative to self-report questionnaires.

Available only for authorised users

Rothman KJ, Greenland S, Lash TL. Validity in epidemiologic studies. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. Philadelphia PA: Lippincott, Williams & Wilkins; 2008. p. 128–47.

Olsen J. Using secondary data. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. Philadelphia PA: Lippincott, Williams & Wilkins; 2008. p. 481–91.

Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health. 1989;79(3):340–9.CrossRefPubMedPubMedCentral

Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879–903. https://doi.org/10.1037/0021-9010.88.5.879.CrossRefPubMed

Kristensen P. Bias from nondifferential but dependent misclassification of exposure and outcome. Epidemiology. 1992;3(3):210–5.CrossRefPubMed

Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Glossary: indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006;60:7–12. https://doi.org/10.1136/jech.2004.023531. CrossRefPubMedPubMedCentral

Piha K, Laaksonen M, Martikainen P, Rahkonen O, Lahelma E. Interrelationships between education, occupational class, income and sickness absence. Eur J Pub Health. 2010;20(3):276–80. https://doi.org/10.1093/eurpub/ckp162.CrossRef

Hansen HT, Ingebrigtsen T. Social class and sickness absence in Norway. Acta Sociologica. 2008;51(4):309–27. https://doi.org/10.1177/0001699308097376.CrossRef

Kristensen TR, Jensen SM, Kreiner S, Mikkelsen S. Socioeconomic status and duration and pattern of sickness absence. A 1-year follow-up study of 2331 hospital employees. BMC Public Health. 2010;10:643. https://doi.org/10.1186/1471-2458-10-643.CrossRefPubMedPubMedCentral

10.

Laaksonen M, Piha K, Rahkonen O, Martikainen P, Lahelma E. Explaining occupational class differences in sickness absence: results from middle-aged municipal employees. J Epidemiol Community Health. 2010;64(9):802–7. https://doi.org/10.1136/jech.2009.093385.CrossRefPubMed

11.

Löve J, Hensing G, Holmgren K, Torén K. Explaining the social gradient in sickness absence: a study of a general working population in Sweden. BMC Public Health. 2013;13(545) http://www.biomedcentral.com/1471-2458/13/545.

12.

Melchior M, Krieger N, Kawachi I, Berkman LF, Niedhammer I, Goldberg M. Work factors and occupational class disparities in sickness absence: findings from the GAZEL cohort study. Am J Public Health. 2005;95(7):1206–12. https://doi.org/10.2105/AJPH.2004.048835.CrossRefPubMedPubMedCentral

13.

Niedhammer I, Chastang JF, David S, Kelleher C. The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey. Soc Sci Med. 2008;67(11):1870–81. https://doi.org/10.1016/j.socscimed.2008.09.007.CrossRefPubMed

14.

North F, Syme SL, Feeney A, Head J, Shipley MJ, Marmot MG. Explaining socioeconomic differences in sickness absence: the Whitehall II study. BMJ. 1993;306(6874):361–6. https://doi.org/10.1136/bmj.306.6874.361.CrossRefPubMedPubMedCentral

15.

Pekkala J, Blomgren J, Pietiläinen O, Lahelma E, Rahkonen O. Occupational class differences in long sickness absence: a register-based study of 2.1 million Finnish women and men in 1996-2013. BMJ Open. 2017;7:e014325. https://doi.org/10.1136/bmjopen-2016-014325.CrossRefPubMedPubMedCentral

16.

Christensen KB, Labriola M, Lund T, Kivimaki M. Explaining the social gradient in long-term sickness absence: a prospective study of Danish employees. J Epidemiol Community Health. 2008;62(2):181–3. https://doi.org/10.1136/jech.2006.056135.CrossRefPubMed

17.

Black D, Sanders S, Taylor L. Measurement of higher education in the census and current population survey. J Am Stat Assoc. 2003;98(463):545–54 https://doi.org/10.1198/016214503000000369.CrossRef

18.

Burdorf A, Post W, Bruggeling T. Reliability of a questionnaire on sickness absence with specific attention to absence due to back pain and respiratory complaints. Occup Environ Med. 1996;53(1):58–62.CrossRefPubMedPubMedCentral

19.

Ferrie E, Kivimäki M, Head J, Shipley MJ, Vahtera J, Marmot MG. A comparison of self-reported sickness absence with absences recorded in employers’ registers: evidence from the Whitehall II study. Occup Environ Med. 2005;62(2):74–9. https://doi.org/10.1136/oem.2004.013896.CrossRefPubMedPubMedCentral

20.

Fredriksson K, Toomingas A, Torgén M, Bildt Thorbjõrnsson C, Kilbom Å. Validity and reliability of self-reported retrospectively collected data on sick leave related to musculoskeletal diseases. Scand J Work Environ Health. 1998;24(5):425–31. https://doi.org/10.5271/sjweh.365. CrossRefPubMed

21.

Severens JL, Mulder J, Laheij RJF, Verbeek ALM. Precision and accuracy in measuring absence from work as a basis for calculating productivity costs in the Netherlands. Soc Sci Med. 2000;51(2):243–9.CrossRefPubMed

22.

Short ME, Goetzel RZ, Pei X, Tabrizi MJ, Ozminkowski RJ, Gibson TB, DeJoy DM, Wilson MG. How accurate are self-reports? Analysis of selfreported health care utilization and absence when compared with administrative data. J Occup Environ Med. 2009;51(7):786–96. https://doi.org/10.1097/JOM.0b013e3181a86671.CrossRefPubMedPubMedCentral

23.

Stapelfeldt CM, Jensen C, Trolle Andersen N, Fleten N, Vinther Nielsen C. Validation of sick leave measures: self-reported sick leave and sickness benefit data from a Danish national register compared to multiple workplace-registered sick leave spells in a Danish municipality. BMC Public Health. 2012;12:661 http://www.biomedcentral.com/1471-2458/12/661.CrossRefPubMedPubMedCentral

24.

Svedberg P, Ropponen A, Lichtenstein P, Alexanderson K. Are self-report of disability pension and long-term sickness absence accurate? Comparisons of self-reported interview data with national register data in a Swedish twin cohort. BMC Public Health. 2010;10:763 https://doi.org/10.1186/1471-2458-10-763.CrossRefPubMedPubMedCentral

25.

van Poppel MNM, de Vet HCW, Koes BW, Smid T, Bouter LM. Measuring sick leave: a comparison of self-reported data on sick leave and data from company records. Occup Med. 2002;52(8):485–90 https://doi.org/10.1093/occmed/52.8.485.CrossRef

26.

Voss M, Stark S, Alfredsson L, Vingård E, Josephson M. Comparisons of self-reported and register data on sickness absence among public employees in Sweden. Occup Environ Med. 2008;65(1):61–7. https://doi.org/10.1136/oem.2006.031427.CrossRefPubMed

27.

Magnus P, Birke C, Vejrup K, Haugan A, Alsaker E, Daltveit A-K, Handal M, Haugen M, Høiseth G, Knudsen GP, Paltiel L, Schreuder P, Tambs K, Vold L, Stoltenberg C. Cohort profile update: the Norwegian Mother and Child Cohort Study (MoBa). Int J Epidemiol. 2016;45(2):382–8. https://doi.org/10.1093/ije/dyw029.CrossRefPubMed

28.

Nilsen RM, Vollset SE, Gjessing HK, Skjærven R, Melve KK, Schreuder P, Alsaker ER, Haug K, Daltveit AK, Magnus P. Self-selection and bias in a large prospective pregnancy cohort in Norway. Paediat Perinat Epidemiol. 2009;23(6):597–608. https://doi.org/10.1111/j.1365-3016.2009.01062.x.CrossRef

29.

Furu K, Karlstad Ø, Skurtveit S, Håberg SE, Nafstad P, London SJ, Nystad W. High validity of mother-reported use of antiasthmatics among children: a comparison with a population-based prescription database. J Clin Epidemiol. 2011;64(8):878–84. https://doi.org/10.1016/j.jclinepi.2010.10.014.CrossRefPubMedPubMedCentral

30.

Skurtveit S, Selmer R, Odsbu I, Handal M. Self-reported data on medicine use in the Norwegian Mother and Child Cohort Study compared to data from the Norwegian prescription database. Nor J Epidemiol. 2014;24(1–2):209–16 https://www.ntnu.no/ojs/index.php/norepid/article/view/1824/1821. Accessed 25 June 2018.

31.

Brantsæter AL, Haugen M, Hagve T-A, Aksnes L, Rasmussen SE, Alexander J, Meltzer HM. Self-reported dietary supplement use in pregnancy corresponds with biological markers – a validation study in the Norwegian Mother and Child Cohort Study. Ann Nutr Metabol. 2007;51(2):146–54. https://doi.org/10.1159/000103275.CrossRef

32.

Brantsæter AL, Haugen M, Rasmussen SE, Alexander J, Samuelsen SO, Meltzer HM. Urine flavonoids and plasma carotenoids in the validation of fruit, vegetable and tea intake during pregnancy in the Norwegian Mother and Child Cohort Study (MoBa). Publ Health Nutr. 2007;10(8):838–47. https://doi.org/10.1017/S1368980007339037. CrossRef

33.

Kvalvik LG, Nilsen RM, Skjærven R, Vollset SE, Midttun Ø, Ueland PM, Haug K. Self-reported smoking status and plasma cotinine concentrations among pregnant women in the Norwegian Mother and Child Cohort Study. Pediatr Res. 2012;723(1):101–7. https://doi.org/10.1038/pr.2012.36. CrossRef

34.

Kristensen P, Bjerkedal T, Irgens LM. Early life determinants of musculoskeletal sickness absence in a cohort of Norwegians born in 1967–1976. Soc Sci Med. 2007;64(3):646–55. https://doi.org/10.1016/j.socscimed.2006.09.032.CrossRefPubMed

35.

Norwegian Institute of Public Health. Norwegian Mother and Child Cohort Study (MoBa). https://www.fhi.no/en/studies/norwegian-mother-and-child-cohort-study. Accessed 25 June 2018.

36.

Inventory of birth cohorts. http://www.birthcohorts.net/ Accessed 25 June 2018.

37.

Norwegian Institute of Public Health. Questionnaires from MoBa. https://www.fhi.no/en/studies/moba/for-forskere-artikler/questionnaires-from-moba/. Accessed 25 June 2018.

38.

Irgens LM. The medical birth registry of Norway. Epidemiological research and surveillance throughout 30 years. Acta Obstet Gynecol Scand. 2000;79(6):435–9. https://doi.org/10.1034/j.1600-0412.2000.079006435.x.CrossRefPubMed

39.

Akselsen A, Lien S, Siverstøl Ø. FD-Trygd: list of variables. SSB Document 2007/5. Oslo-Kongsvinger: Statistics Norway; 2007. https://www.ssb.no/a/english/publikasjoner/pdf/doc_200705_en/doc_200705_en.pdf. Accessed 25 June 2018.

40.

Statistics Norway. Norwegian Standard Classification of Education (Revised 2000). Official Statistics of Norway C751. Oslo-Kongsvinger: Statistics Norway; 2003. http://www.ssb.no/english/subjects/04/90/nos_c751_en/nos_c751_en.pdf. Accessed 25 June 2018.

41.

Statistics Norway Den individbaserte utdanningsstatistikken. Dokumentasjon 2005. [In Norwegian]. Official Statistics of Norway, D351. Oslo-Kongsvinger: Statistics Norway; 2006. https://ssb.no/a/publikasjoner/pdf/nos_d351/nos_d351.pdf. Accessed 25 June 2018.

42.

Chan TW, Boliver V. The grandparents effect in social mobility: evidence from British birth cohort studies. Am Sociol Rev 2013;78(4):662–78. https://doi.org/10.1177/0003122413489130.CrossRef

43.

Kristensen P, Corbett K, Mehlum IS. The gender gap in sickness absence from work and the influence of parental absence on offspring absence 15 years later: register-based cohort of Norwegians born in 1974-1976. BMC Public Health. 2015. https://doi.org/10.1186/s12889-015-2037-2 http://www.biomedcentral.com/1471-2458/15/684.

44.

StatBank Norway. Labour market and earnings; Employment, register-based. https://www.ssb.no/en/statistikkbanken. Accessed 25 June 2018.

45.

Lawlor DA, Nybo Andersen A-M, Batty GD. Birth cohort studies: past, present and future. Int J Epidemiol. 2009;8(4):897–902. https://doi.org/10.1093/ije/dyp240.CrossRef

46.

Poulsen G, Strandberg-Larsen K, Mortensen LH, Barros H, Cordier S, Correia S, Danileviciute A, van Eijsden M, Fernández-Somoano A, Gehring U, Grazuleviciene R, Hafkamp-de Groen E, Henriksen TB, Larrañaga I, Magnus P, Pickett K, Raat H, Richiardi L, Rouget F, Rusconi F, Stoltenberg C, Uphoff EP, Vrijkotte TGM, Wijga AH, Vrijheid M, Osler M, Nybo Andersen A-M. Exploring educational disparities in risk of preterm delivery: a comparative study of 12 European birth cohorts. Paediat Perinat Epidemiol. 2015;29(3):172–83. https://doi.org/10.1111/ppe.12185.CrossRef

47.

Zambrana IM, Ystrom E, Pons F. Impact of gender, maternal education, and birth order on the development of language comprehension: a longitudinal study from 18 to 36 months of age. J Dev Behav Pediat. 2012;33(2):146–55. https://doi.org/10.1097/DBP.0b013e31823d4f83.

48.

Kristensen P, Nordhagen R, Wergeland E, Bjerkedal T. Job adjustment and absence from work in mid-pregnancy in the Norwegian Mother and Child Cohort Study (MoBa). Occup Environ Med. 2008;65:560–6. https://doi.org/10.1136/oem.2007.035626.CrossRefPubMed

49.

Hansen ML, Thulstrup AM, Juhl M, Kristensen JK, Ramlau-Hansen CH. Occupational exposures and sick leave during pregnancy: results from a Danish cohort study. Scand J Work Environ Health. 2015;41(4):397–406. https://doi.org/10.5271/sjweh.3507.CrossRefPubMed

Title: Information bias of social gradients in sickness absence: a comparison of self-report data in the Norwegian Mother and Child Cohort Study (MoBa) and data in national registries
Authors: Petter Kristensen
Karina Corbett
Ferdinand A. Mohn
Therese N. Hanvold
Ingrid S. Mehlum
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: BMC Public Health / Issue 1/2018
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-018-6208-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Information bias of social gradients in sickness absence: a comparison of self-report data in the Norwegian Mother and Child Cohort Study (MoBa) and data in national registries

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2018

The effect of contextual factors on unintentional injury hospitalization: from the Korea National Hospital Discharge Survey

Knowledge, perception and attitude about Crimean Congo Hemorrhagic Fever (CCHF) among medical and pharmacy students of Pakistan

Implementation of 100% smoke-free law in Uganda: a qualitative study exploring civil society’s perspective

Informing tobacco control policy in Jordan: assessing the effectiveness of pictorial warning labels on cigarette packs

Social network and HIV risk behaviors in female sex workers: a systematic review

Smokers’ unprompted comments on cigarette additives during conversations about the genetic basis for nicotine addiction: a focus group study