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Comparing the sociodemographic characteristics of participants and non-participants in the population-based Tromsø Study



Differences in the sociodemographic characteristics of participants and non-participants in population-based studies may introduce bias and reduce the generalizability of research findings. This study aimed to compare the sociodemographic characteristics of participants and non-participants of the seventh survey of the Tromsø Study (Tromsø7, 2015–16), a population-based health survey.


A total of 32,591 individuals were invited to Tromsø7. We compared the sociodemographic characteristics of participants and non-participants by linking the Tromsø7 invitation file to Statistics Norway, and explored the association between these characteristics and participation using logistic regression. Furthermore, we created a geographical socioeconomic status (area SES) index (low-SES, medium-SES, and high-SES area) based on individual educational level, individual income, total household income, and residential ownership status. We then mapped the relationship between area SES and participation in Tromsø7.


Men, people aged 40–49 and 80–89 years, those who were unmarried, widowed, separated/divorced, born outside of Norway, had lower education, had lower income, were residential renters, and lived in a low-SES area had a lower probability of participation in Tromsø7.


Sociodemographic differences in participation must be considered to avoid biased estimates in research based on population-based studies, especially when the relationship between SES and health is being explored. Particular attention should be paid to the recruitment of groups with lower SES to population-based studies.

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Population-based studies are important, as they are often used as a source of data on determinants of health and as a source of information on people’s health status [1]. As such, these surveys should adequately reflect the target population for the relevant indicators. A problem with population-based studies is that participation is voluntary, thus people can choose not to participate. Non-participation can reduce the precision of estimates, and more seriously may introduce selection bias if both the exposure and the outcome under investigation affect the probability of participation, and may reduce the generalizability of the results [2]. The presence of selection bias cannot usually be inferred from the study data alone; participation studies are therefore necessary to identify any underrepresented subgroups [3]. Knowledge of the characteristics of non-participants may help to improve recruitment procedures and representativeness, leading to more accurate assumptions and conclusions in population-based studies, i.e., estimations of prevalence and incidence, and associations between exposures and outcomes.

Sociodemographic characteristics refer to a combination of social and demographic factors [4], including socioeconomic status (SES), which is often measured by an individual’s educational attainment, occupation, and income [5]. Individuals with low SES have been reported to have poorer health status and to be less likely to participate in health surveys compared with individuals with high SES [6,7,8,9,10]. Men, people who are unmarried, and those with low education or low income are also less likely to participate, according to previous studies [10,11,12,13]. The association between participation and age [14,15,16] or belonging to an ethnic minority [11, 17] is inconsistent in the literature.

National registers with high-quality individual-level data can be useful in providing information on non-participants, which can be compared with information on participants. The present study used register data to compare the sociodemographic characteristics of participants and non-participants of the seventh survey of the Tromsø Study (Tromsø7).


Study population

The Tromsø Study is an ongoing population-based health survey. It currently consists of seven surveys (Tromsø1-7) conducted between 1974 and 2016 in the municipality of Tromsø, Northern Norway. The study population consists of complete birth cohorts and random samples [18, 19]. Tromsø7 was carried out between 2015 and 2016, inviting all inhabitants aged 40 years and above in the municipality of Tromsø to participate. A total of 32,591 eligible individuals were invited and 65% participated in Tromsø7 [20].

Linkage to statistics Norway

Information on sociodemographic characteristics recorded in Statistics Norway (SSB), which covers the entire Norwegian population, was linked with data from the Tromsø7 invitation file, which covered all 32,591 invited individuals, using the unique 11-digit personal identification number assigned to each resident of Norway at birth or immigration. SSB performed the linkage and all personal identification numbers were deleted.

Sociodemographic characteristics of participants and non-participants

All sociodemographic characteristics of participants and non-participants of Tromsø7 were taken from the SSB, including age (10-years age intervals), sex, and marital status (married, unmarried, widow(er), and divorced/separated). The category “divorced/separated” included the subgroups separated (n = 517), separated partnership (n = 4), and divorced partner (n = 25). The category “married” included registered partnerships (n = 20). Data was also collected on country of birth, which was categorized into four broad groups: Norway, Western countries (Western Europe, North America, and Oceania), Eastern Europe (including Russia), and other countries (Asia, Africa, and South America). Individuals born in Norway were further categorized into three regions of birth: Tromsø, Northern Norway (Finnmark, Troms, and Nordland), and South Norway (counties south of Nordland). Finally, information was extracted on the highest completed educational level (primary education, upper secondary education, college/university < 4 years; and college/university ≥ 4 years), income (defined as individual income and total household income and categorized as in the Tromsø Study questionnaire: ≤ 250,000 Norwegian kroner (NOK) to ≥ 750,000 NOK), and residential ownership status (owner or renter).

Statistical analyses

Descriptive characteristics were presented as number (percent). Sex-specific binary logistic regression analyses were used to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) of participation in unadjusted and age-adjusted models. The variable area SES was adjusted for individual-level socioeconomic status.

Individual-level SES was calculated based on educational level, individual income, total household income, and residential ownership status. For each of these four variables, a Z-score was calculated and then summarized to give an individual-level SES score. We also created a geographical SES index, based on 36 geographical subdivisions of the municipality of Tromsø defined in a local Public Health report [21]. These geographical subdivisions are based on the basic geographical and statistical units of the municipality of Tromsø, in order to establish small, stable geographical units that give a flexible basis for the presentation of regional statistics [22]. The geographical SES index was calculated as the average individual-level SES score in each of the geographical subdivisions, resulting in a continuous variable ranging from -1.73 to 1.24, and then categorized as low-SES area, medium-SES area, or high-SES area, based on tertiles using the command xtile in the statistical program Stata. Participation in Tromsø7 within each of the 36 geographical subdivisions was also divided into tertiles: low (59.3%), medium (66.7%), and high (68.5%), and the spatial distribution of SES areas and participation in the 36 geographical subdivisions was graphed using choropleth maps.

Analyses were performed in Stata 16.0 (StataCorp, College Station, TX, USA). Choropleth maps were created in Python 3 (using mainly the pandas, geopandas, and plotly express packages). A GeoJSON file was collected from the Norwegian Mapping Authority [23], while a base map from OpenStreetMap [24] was used.


A total of 32,591 individuals were invited to Tromsø7, of which 11,508 (35%) did not participate. The mean age of participants and non-participants was 57.3 years and 57.6 years, respectively. The median individual and total household income for participants were 431,799 NOK (IQR: 8680—585,830 NOK) and 725,354 NOK (IQR: 489,059—943,548 NOK), respectively. The corresponding figures for non-participants were 244,083 NOK (IQR: 0 – 524,675 NOK) and 546,086 NOK (IQR: 321,302 – 831,602 NOK). The sociodemographic distribution of participants differed from that of non-participants (Table 1). In both women and men, those who were unmarried, widowed, separated/divorced, born outside of Norway, had lower education, had lower income, were residential renters, and lived in a low-SES area had a lower probability of participation (Fig. 1 and Supplementary Table 1).

Table 1 Distribution of sociodemographic characteristics among participants and non-participants by sex, Tromsø7 (2015–2016)
Fig. 1
figure 1

Age-adjusted odds ratios for participation by sex, Tromsø7 (2015–2016). *Reference group. **Additionally adjusted for individual-level socioeconomic status

Men were less likely to participate than women (age-adjusted OR 0.79, 95% Cl 0.75 – 0.82, analysis not shown). Invitees aged 80–99 years were less likely to participate (women: OR 0.27, 95% Cl 0.24 – 0.31; men: OR 0.76, 95% Cl 0.65 – 0.89) compared to the youngest age group (40–49 years) and other age groups. However, the youngest age group was less likely to participate than those aged 50–79 years in both sexes. The odds of participation were highest among those with an educational level of college/university < 4 years, for both women (OR 2.20, 95% Cl 1.99 – 2.42) and men (OR 2.22, 95% Cl 2.00 – 2.47).

Participation decreased with decreasing individual and total household income for men. Among women, those with medium individual income (450,000–549,999 NOK) were more likely to participate than those with the highest individual income, while women with lowest individual income were less likely to participate. Lastly, individuals living in medium- and high-SES areas had higher odds to participate than those living in low-SES areas, after adjustment for individual-level SES. However, the estimated effect of area SES was not very strong (women: OR 1.24, 95% Cl 1.13 – 1.35; men: OR 1.17, 95% Cl 1.08 – 1.28). Individual-level SES showed a stronger effect, and those with high individual-level SES were around three times more likely to participate than those with low individual-level SES, in both sexes.

Generally, individuals living in high-SES areas, located on the West side of the city, had higher participation. None of the low-SES areas had high participation, but not all high SES areas had high participation, and there was more variation in participation in medium-SES areas (Fig. 2).

Fig. 2
figure 2

Choropleth maps of socioeconomic status (SES) areas (A) and participation (%) in the Tromsø Study (B) in 36 subdivisions of the municipality Tromsø, Tromsø7 (2015–2016). Maps: Kartverket (CC-BY 4.0), Carto/OpenStreetMap©(CC BY-SA 2.0), MapBox©. SES area based on the average individual-level SES score in each geographical subdivision [21]. SES: socioeconomic status


This study showed that men, people aged 49–49 and 80–89 years, those who were unmarried, widowed, separated/divorced, born outside of Norway, had lower education, had lower income, were residential renters, and lived in a low-SES area had a lower probability of participation in Tromsø7.

In accordance with results from Norwegian [9, 25, 26], Finnish [27, 28], and Dutch [29] studies, our study found that men were less likely to participate than women. In a previous Finnish study, women were found to engage more frequently in health behavior and to seek health-related information more often than men [30]. The tendency of men to have lower interest in participating in population-based studies has also been shown previously [31], and previous surveys of the Tromsø Study have had lower participation among men [18, 19]. In an attempt to increase participation among men in the age group 40–49 years, they were specifically targeted during the planning of Tromsø7 [20].

In the literature, evidence regarding study participation and age is much less consistent. We found that people aged 40–49 and 80–99 years were less likely to participate, whereas some studies have found that age does not affect participation [16], others found that individuals (40–49 years old) were more likely to participate [15], and still others found higher participation among older (> 60 years) individuals [14, 32]. Less participation among the oldest age group could be associated with poorer health among the very old [27, 28]; however, findings from another study suggested that older people’s health conditions do not affect survey participation [33]. Different explanations for participation in health surveys have been explored earlier [31, 34, 35]. Older persons (≥ 65 years) think that it is a civic duty to participate in population-based research, while lower participation among younger individuals may be due to a lack of time and a perception that their health is good [31, 34].

It has been suggested that marriage may encourage positive health behaviors, which over time cumulate and facilitate desirable health outcomes [36]. We observed that people with marital statuses other than married were less likely to participate than married individuals of both sexes. This is in accordance with other population-based studies [16, 25, 37]. Previous studies have highlighted the increased health and survival among married individuals compared to unmarried individuals [38, 39], which seems to be the case for men in particular [39, 40]. A possible explanation was proposed in a qualitative study on participants and non-participants of community health screening, which found that the decision to participate in screening is often made by a partner [41]. Sala et al. [33] reported that, among couples, if one partner took part in a health survey the other was more likely to respond as well.

According to several studies, participants born in the country where a survey is conducted are more likely to participate than those born outside of the country [9, 11, 12, 15]. Even though the municipality of Tromsø is currently the 12th most populous in Norway, it has relatively few immigrants (16%, year 2021) compared to other populous municipalities in the country [42, 43]. Furthermore, the Tromsø Study questionnaires are in Norwegian, and to participate in the Tromsø Study, individuals had to master the Norwegian language. In an Australian study, speaking the same language at home as was used in the questionnaire was associated with higher odds of participation [15]. This indicates that language difficulties hinder participation.

In our study, increased educational level, total household income, and being a residential owner were all socioeconomic factors associated with an increased probability of participation. Prior literature has also reported that participation was more likely among individuals with high educational level, income [7,8,9,10,11, 44], and among residential owners [14, 15, 37]. Bopp et al. [37] suggested that residential owners are more likely to participate because they move less frequently, and are therefore easier to track. Education is considered an important social determinant of health, as it helps to promote and sustain healthy lifestyles and positive health choices [45]. Nadelsen et al. [46] found that as years of college increased, trust in science also increased. Furthermore, the authors suggested that people with more education are more likely to have a deeper understanding of science and the work of scientists, and are thus more likely to be engaged in critical examinations of scientific issues. For instance, UiT The Arctic University of Norway and The University Hospital of North Norway are among the largest public workplaces in the municipality of Tromsø [47], and their employees belong to occupational groups with a higher educational level. As research is as a part of their work tasks, they have an deeper understanding of science and their willingness to participate might be higher than that observed in other workplaces. In addition, different employers in Tromsø were asked to give their employees time off from work to participate in Tromsø7 [48]. Indeed, a Norwegian qualitative study showed that reasons for not participating in a population-based study included difficulty in taking a day off from work and loss of salary during participation [34]. This might apply especially to individuals in low-income groups, as they are more financially vulnerable than those with higher income. Furthermore, in this qualitative study, an informant suggested that if people were to get paid by their employer to participate in health research, more people might participate [34]. Some have suggested providing modest financial compensation for lost work time and travel expenses as a token of appreciation to increase participation rates [28, 34]. Olsen et al. [49] found that a scratch lottery ticket incentive increased participation among individuals with lower education, and this might apply to low-income groups as well. However, these approaches are expensive, especially for a population-based study whose target group is the entire general population.

Individuals with low SES do not only participate less compared to individuals with high SES, but their participation decreases over time, according to a follow-up study of a randomized controlled trial [50]. Indeed, participation in health surveys decreases over time in all educational levels, though the decline seems fastest for those with low education [44].

It has been suggested that participation may not depend only on individual characteristics, but also on geographical features [51]. The SES of the surrounding area has been reported to be associated with lower participation in cohort studies [14, 16, 51]. This is consistent with our findings, which showed that those living in high-SES areas were more likely to participate than those living in low-SES areas. Sala et al. [33] found that participation among women was associated with their socioeconomic background and the wealth of their residential area. Bender et al. [52] hypothesize that residents of deprived neighborhoods may be lack of social support to participate in health check and they may also be less trusting of public health authorities.

The literature is generally consistent in showing that those living in economically disadvantaged areas have poorer health [53, 54]. For instance, the prevalence of diabetes mellitus has been found to be lower for those living in areas with medium and high SES for both women and men, compared to those living in areas with low SES [55]. Those who volunteer to participate in health surveys are often more likely to have favorable exposures and health profiles compared to those who do not [14, 56, 57]. Sociodemographic differences in participation can lead to bias in the population-level estimates and in the associations with health status and health behaviors. For instance, low educational level and low income are both positively associated with unhealthy dietary habits [58]. If low educational level is also associated with non-participation, as we have shown, and unhealthy dietary habits are associated with the probability of participation, any resultant associations will be biased (selection bias). A false conclusion might thus be drawn about the health status of the population. Furthermore, non-participation can lead to an underestimation of the prevalence of health indicators and harmful health behaviors [29], as well as reduced precision of estimates.

Efforts should be made to recruit subgroups that we have shown to be underrepresented in our study. For example, our findings show non-participation by area SES. This can help the Tromsø Study and other population-based studies when planning recruitment for future surveys. However, sending extra reminders has shown little impact on the sociodemographic distribution among participants, so other methods to increase the participation of underrepresented groups should be explored [59].

Strength and limitations

The main strength of this study is the linkage of information on sociodemographic characteristics from the Tromsø Study to that from the SSB, a national register. Another strength is the use of a large population-based study with reasonably high participation. Our study provides an overview of the representativeness of the Tromsø Study regarding a variety of sociodemographic characteristics. A potential limitation of this study is that we have categorized continuous variables; as there are no perfect cut-off points for variables like income, information might be lost in categorization. Errors in the collection and processing of the income data are unavoidable, even for administrative data. Income information for employed individuals was based on complete registration from employers and other administrative data. An extensive work from SSB has been carried out to minimize errors, and we consider errors to be relatively insignificant. Whereas information from self-employed individuals in Norway is self-reported, but they are required to provide accurate taxable income, which is carefully controlled by the tax authorities.

In conclusion, sociodemographic differences in participation must be considered to avoid biased estimates in research based on population-based studies, especially when the relationship between SES and health is being explored. Particular attention should be paid to the recruitment of groups with lower SES to population-based studies.

Availability of data and materials

The data that support the findings of this study are available from the Statistics Norway (SSB), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.



Confidents intervals


Data Protection Impact Assessment


Interquartile range


The Norwegian Data Protection Authority


Odds ratio

REK North:

The Regional Committee for Medical and Health Research Ethics North


Socioeconomic status


Statistics Norway


  1. Ezzati-Rice TM, Curtin LR. Population-based surveys and their role in public health11Address reprint requests to: Centers for Disease Control and Prevention, National Immunization Program Resource Center, 1600 Clifton Road NE, Mailstop E-34, Atlanta, Georgia 30333. Fax: (404) 639–8828.  Am J Prev Med. 2001;20(4, Supplement 1):15–6.

    Article  CAS  PubMed  Google Scholar 

  2. Jousilahti P, Salomaa V, Kuulasmaa K, Niemelä M, Vartiainen E. Total and cause specific mortality among participants and non-participants of population based health surveys: a comprehensive follow up of 54 372 Finnish men and women. J Epidemiol Community Health. 2005;59(4):310.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lash TL, Rothman KJ, editors. Selection Bias and Generalizability. Philadelphia: Lippincott Williams and Wilkins; 2021. p. 315–31.

  4. Hatch SL, Frissa S, Verdecchia M, Stewart R, Fear NT, Reichenberg A, et al. Identifying socio-demographic and socioeconomic determinants of health inequalities in a diverse London community: the South East London Community Health (SELCoH) study. BMC Public Health. 2011;11(1):861.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358(23):2468–81.

    Article  CAS  PubMed  Google Scholar 

  6. Lorant V, Demarest S, Miermans P-J, Van Oyen H. Survey error in measuring socio-economic risk factors of health status: a comparison of a survey and a census. Int J Epidemiol. 2007;36(6):1292–9.

    Article  PubMed  Google Scholar 

  7. 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):449–54.

    Article  PubMed  PubMed Central  Google Scholar 

  8. McElfish PA, Long CR, Selig JP, Rowland B, Purvis RS, James L, et al. Health Research Participation, Opportunity, and Willingness Among Minority and Rural Communities of Arkansas. Clin Transl Sci. 2018;11(5):487–97.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Søgaard 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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tolonen H, Helakorpi S, Talala K, Helasoja V, Martelin T, Prättälä R. 25-year trends and socio-demographic differences in response rates: Finnish adult health behaviour survey. Eur J Epidemiol. 2006;21(6):409–15.

    Article  PubMed  Google Scholar 

  11. Christensen AI, Lau CJ, Kristensen PL, Johnsen SB, Wingstrand A, Friis K, Davidsen M, Andreasen AH. The Danish National Health Survey: Study design, response rate and respondent characteristics in 2010, 2013 and 2017. Scandinavian journal of public health. 2022;50(2):180–188.

  12. Ahlmark N, Algren MH, Holmberg T, Norredam ML, Nielsen SS, Blom AB, et al. Survey nonresponse among ethnic minorities in a national health survey–a mixed-method study of participation, barriers, and potentials. Ethn Health. 2015;20(6):611–32.

    Article  PubMed  Google Scholar 

  13. Martikainen P, Laaksonen M, Piha K, Lallukka T. Does survey non-response bias the association between occupational social class and health? Scand J Public Health. 2007;35(2):212–5.

    Article  PubMed  Google Scholar 

  14. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Livingston P, Lee S, Taylor H. A comparison of participants with non-participants in a population-based epidemiologic study: The Melbourne Visual Impairment Project. [Lisse, the Netherlands]. 1997. p. 73–81.

    Google Scholar 

  16. Goldberg M, Chastang JF, Leclerc A, Zins M, Bonenfant S, Bugel I, et al. Socioeconomic, Demographic, Occupational, and Health Factors Associated with Participation in a Long-term Epidemiologic Survey: A Prospective Study of the French GAZEL Cohort and Its Target Population. Am J Epidemiol. 2001;154(4):373–84.

    Article  CAS  PubMed  Google Scholar 

  17. Wendler D, Kington R, Madans J, Wye GV, Christ-Schmidt H, Pratt LA, et al. Are racial and ethnic minorities less willing to participate in health research? PLoS Med. 2006;3(2): e19.

    Article  PubMed  Google Scholar 

  18. Eggen AE, Mathiesen EB, Wilsgaard T, Jacobsen BK, Njolstad I. The sixth survey of the Tromso Study (Tromso 6) in 2007–08: collaborative research in the interface between clinical medicine and epidemiology: study objectives, design, data collection procedures, and attendance in a multipurpose population-based health survey. Scand J Public Health. 2013;41(1):65–80.

    Article  PubMed  Google Scholar 

  19. Jacobsen BK, Eggen AE, Mathiesen EB, Wilsgaard T, Njolstad I. Cohort profile: the Tromso Study. Int J Epidemiol. 2012;41(4):961–7.

    Article  PubMed  Google Scholar 

  20. Hopstock LA, Grimsgaard S, Johansen H, Kanstad K, Wilsgaard T, Eggen AE. The seventh survey of the Tromsø Study (Tromsø7) 2015–2016: study design, data collection, attendance, and prevalence of risk factors and disease in a multipurpose population-based health survey. Scand J Public Health. 2022;50(7):919–29.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hopstock L, Løvsletten O, Johansen H, Tiwari S, Njølstad I, Løchen M-L. Folkehelserapport: Den sjuende Tromsøundersøkelsen 2015–16. Septentrio Reports. 2019.

    Google Scholar 

  22. Statistics Norway. Basic statistical unit. Available from:  [Accessed 20 June 2022].

  23. GeoNorge. Statistiske enheter grunnkretser. Available from: [Accessed 09 May 2022].

  24. OpenStreetMap. Base map. Available from:  [Accessed 09 May 2022].

  25. Langhammer A, Krokstad S, Romundstad P, Heggland J, Holmen J. The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms. BMC Med Res Methodol. 2012;12(1):143.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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):1306–14.

    Article  PubMed  Google Scholar 

  27. Nummela O, Sulander T, Helakorpi S, Haapola I, Uutela A, Heinonen H, et al. Register-based data indicated nonparticipation bias in a health study among aging people. J Clin Epidemiol. 2011;64(12):1418–25.

    Article  PubMed  Google Scholar 

  28. Tolonen H, Lundqvist A, Jääskeläinen T, Koskinen S, Koponen P. Reasons for non-participation and ways to enhance participation in health examination surveys—the Health 2011 Survey. Eur J Pub Health. 2017;27(5):909–11.

    Article  Google Scholar 

  29. Van Loon AJM, Tijhuis M, Picavet HSJ, Surtees PG, Ormel J. Survey non-response in the Netherlands: effects on prevalence estimates and associations. Ann Epidemiol. 2003;13(2):105–10.

    Article  PubMed  Google Scholar 

  30. Ek S. Gender differences in health information behaviour: a Finnish population-based survey. Health Promot Int. 2013;30(3):736–45.

    Article  PubMed  Google Scholar 

  31. Enzenbach C, Wicklein B, Wirkner K, Loeffler M. Evaluating selection bias in a population-based cohort study with low baseline participation: the LIFE-Adult-Study. BMC Med Res Methodol. 2019;19(1):135.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Laaksonen M, Aittomäki A, Lallukka T, Rahkonen O, Saastamoinen P, Silventoinen K, et al. Register-based study among employees showed small nonparticipation bias in health surveys and check-ups. J Clin Epidemiol. 2008;61(9):900–6.

    Article  PubMed  Google Scholar 

  33. Sala E, Zaccaria D, Guaita A. Survey participation to the first Wave of a longitudinal study of older people: the case of the Italian InveCe. Ab study. Qual Quan. 2020;54(1):99–110.

    Article  Google Scholar 

  34. Antonsen S. Motivasjon for deltakelse i helseundersøkelser. Nor J Epidemiol [Internet]. 2009;15(1). Available from: [cited 23 May 2023].

  35. Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17(9):643–53.

    Article  PubMed  Google Scholar 

  36. Kiecolt-Glaser JK, Newton TL. Marriage and health: his and hers. Psychol Bull. 2001;127(4):472.

    Article  CAS  PubMed  Google Scholar 

  37. Bopp M, Braun J, Faeh D, Group ftSNCS. Variation in Mortality Patterns Among the General Population, Study Participants, and Different Types of Nonparticipants: Evidence From 25 Years of Follow-up. Am J Epidemiol. 2014;180(10):1028–35.

    Article  PubMed  Google Scholar 

  38. Hu YR, Goldman N. Mortality differentials by marital status: an international comparison. Demography. 1990;27(2):233–50.

    Article  CAS  PubMed  Google Scholar 

  39. Robards J, Evandrou M, Falkingham J, Vlachantoni A. Marital status, health and mortality. Maturitas. 2012;73(4):295–9.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ben-Shlomo Y, Smith GD, Shipley M, Marmot MG. Magnitude and causes of mortality differences between married and unmarried men. J Epidemiol Community Health. 1993;47(3):200–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Engebretson J, Mahoney JS, Walker G. Participation in community health screenings: a qualitative evaluation. J Community Health Nurs. 2005;22(2):77–92.

    Article  PubMed  Google Scholar 

  42. Statistics Norway. Norges 100 mest folkerike kommune 2021. Available from: [Accessed 10 Aug 2022].

  43. Gulbrandsen F, Kulasingam AS, Molstad CS, Steinkellner A. Innvandrere og norskfødte med innvandrerforeldres fordeling på kommunenivå. Statistisk sentralbyrå: Statistisk sentralbyrå; 2021.

    Google Scholar 

  44. Reinikainen J, Tolonen H, Borodulin K, Härkänen T, Jousilahti P, Karvanen J, et al. Participation rates by educational levels have diverged during 25 years in Finnish health examination surveys. Eur J Pub Health. 2017;28(2):237–43.

    Article  Google Scholar 

  45. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Health. 2020;78(1):20.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Nadelson L, Jorcyk C, Yang D, Jarratt Smith M, Matson S, Cornell K, et al. I just don’t trust them: the development and validation of an assessment instrument to measure trust in science and scientists. Sch Sci Math. 2014;114(2):76–86.

    Article  Google Scholar 

  47. Tromsø Municipality. Fakta om Tromsø. Available from:  [Accessed 20 June 2022].

  48. Johansen H. Sluttrapport Tromsø 7: Den sjuende Tromsøundersøkelsen 2015–16. 2019.

    Google Scholar 

  49. Olsen F, Abelsen B, Olsen JA. Improving response rate and quality of survey data with a scratch lottery ticket incentive. BMC Med Res Methodol. 2012;12(1):52.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bender A, Jørgensen T, Helbech Kleist B, Linneberg A, Pisinger C. Socioeconomic position and participation in baseline and follow-up visits: The Inter99 study. Eur J Prev Cardiol. 2012;21:899–905.

    Article  PubMed  Google Scholar 

  51. Chaix B, Billaudeau N, Thomas F, Havard S, Evans D, Kestens Y, et al. Neighborhood effects on health: correcting bias from neighborhood effects on participation. Epidemiology. 2011;22(1):18–26.

    Article  PubMed  Google Scholar 

  52. Bender AM, Kawachi I, Jørgensen T, Pisinger C. Neighborhood deprivation is strongly associated with participation in a population-based health check. PLoS ONE. 2015;10(6): e0129819.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Diez Roux AV, Jacobs DR, Kiefe CI. Neighborhood characteristics and components of the insulin resistance syndrome in young adults: the coronary artery risk development in young adults (CARDIA) study. Diabetes Care. 2002;25(11):1976–82.

    Article  PubMed  Google Scholar 

  54. Howard VJ, McClure LA, Kleindorfer DO, Cunningham SA, Thrift AG, Roux AVD, et al. Neighborhood socioeconomic index and stroke incidence in a national cohort of blacks and whites. Neurology. 2016;87(22):2340–7.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bilal U, Hill-Briggs F, Sanchez-Perruca L, Del Cura-Gonzalez I, Franco M. Association of neighbourhood socioeconomic status and diabetes burden using electronic health records in Madrid (Spain): the HeartHealthyHoods study. BMJ Open. 2018;8(9): e021143.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Drivsholm T, Eplov LF, Davidsen M, Jørgensen 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):623–31.

    Article  PubMed  Google Scholar 

  57. Tolonen H, Dobson A, Kulathinal S. Effect on trend estimates of the difference between survey respondents and non-respondents: results from 27 populations in the WHO MONICA Project. Eur J Epidemiol. 2005;20(11):887–98.

    Article  PubMed  Google Scholar 

  58. Baraldi LG, Steele EM, Canella DS, Monteiro CA. Consumption of ultra-processed foods and associated sociodemographic factors in the USA between 2007 and 2012: evidence from a nationally representative cross-sectional study. BMJ Open. 2018;8(3): e020574.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Selmer R, Sögaard AJ, Bjertness E, Thelle D. The Oslo Health Study: reminding the non-responders - effects on prevalence estimates. Norsk epidemiologi. 2003;13(1):89.

    Google Scholar 

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We sincerely thank participants for participating in the Tromsø Study. We thank Dr Sweta Tiwari for meaningful discussions regarding the categorization of area SES.


Open access funding provided by UiT The Arctic University of Norway (incl University Hospital of North Norway). This study received funding from the High North Population Studies, UiT The Arctic University of Norway.

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Authors and Affiliations



All authors contributed to the study conception and design. Material preparation and analysis were performed by CQV. Project administration was performed by CQV and AEE. Choropleth maps were created by PJS with Python 3. The first draft of the manuscript was written by CQV, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chi Quynh Vo.

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Ethics approval and consent to participate

This study is based on secondary use of data from administrative registry. This study was not defined as health research by The Regional Committee for Medical and Health Research Ethics North (REK North) and thus was exempted from the requirement of study preapproval. We have written consent from participants. For non-participants who have not consented in the project, the legal basis for the processing will be the performance of a task in the public interest. The project has therefore conducted a Data Protection Impact Assessment (DPIA) and the project was approved on 22.11.2021 by Data Protection Officer at UiT The Arctic University of Norway (ref. 809230).

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Supplementary Table 1.

Odds ratios for participation by sex, Tromsø7 (2015-2016).

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Vo, C.Q., Samuelsen, PJ., Sommerseth, H.L. et al. Comparing the sociodemographic characteristics of participants and non-participants in the population-based Tromsø Study. BMC Public Health 23, 994 (2023).

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