The Oslo Health Study consists of a central core and 70 supplementary projects. The data collection for the core, which we report in the present paper, was undertaken following the procedure mentioned below:

A letter of invitation – containing an information brochure and the main questionnaire – was mailed two weeks prior to the appointment at the screening station. Information included, among other things, that participants could avail themselves of the brochure and the questionnaire, in 11 other languages.

At the screening station a simple clinical examination was conducted: A venous non-fasting blood sample was analysed for serum total cholesterol, HDL cholesterol, glucose and triglycerides. Automatic device (DINAMAP) measured pulse recordings, systolic- and diastolic blood pressures. Body weight (in kilograms), height (in cm) and waist-hip-ratio (cm) were measured with a standard procedure according to the protocol 25.

At the screening station the main questionnaire was handed in and the participants were given two supplementary questionnaires, which they were requested to fill in at home and return by mail in pre-addressed pre-stamped envelopes (more details are presented at HUBRO's web site 24).

Four weeks after attending the examination, a letter with the results of the examination and blood tests was sent to all participants. Those presenting with high risk of cardiovascular disease 25 were offered a new clinical examination at Ullevål University Hospital.

An extensive information campaign was developed in order to motivate those invited to attend 24.

Of the 40 888 citizens invited, a total of 18 770 individuals (46 %) participated in the survey.

Almost all those attending the screening station responded to the main questionnaire, while the response rates to the first and second supplementary questionnaire were 82 %-87 % of those attending. Table 1 shows the participation rates in different subgroups of age and gender. The notation "attendees" and "non-attendees" used later refer to participants and non-participants. Further detailed information concerning participation in the Oslo Health Study can be obtained from HUBRO's web site 24.

All the participants of the Oslo Health Study have given their written consent. The participant's names and personal ID numbers are omitted when data are used for research purposes. The Norwegian Data Inspectorate has approved the Oslo Health Study, the Regional Committee for Medical Research Ethics has evaluated it, and it has been conducted in full accordance with the World Medical Association Declaration of Helsinki.

Sociodemographic information from public registers in Statistics Norway 26 was linked to data from the clinical examination, the main- and first supplementary questionnaire and to the population file used for invitation to the Oslo Health Study – through the individual's personal identification number. All personal identification has been erased before the data is analysed.

The following variables were added from the Statistics Norway's event-history database: highest education completed, personal income and information about disability -, rehabilitation -, sickness -, unemployment and single parent benefit. From the invitation file, also obtained from Statistics Norway, we used the following variables in the present paper: age, gender, marital status, country of birth and residential region.

From the clinical examination and the main questionnaire we have used:

Body mass index: Body weight (kilogram)/(height (meter))²

Self-evaluated general health status: How would you describe your present state of health? (poor, not very good, good, very good)

Self-reported daily smoking: Have you smoked or do you smoke daily? (yes – now, yes – earlier, never)

Mental distress: Below is a list of various problems. Have you suffered from any of the following during the last week (including today)? (Put a cross for every problem)

The 10 items asked 27 are an abridged version of the Hopkins Symptom Check List (HSCL) 28 which is a widely used, self-administered instrument designed to measure psychological distress in population surveys. The HSCL-10 consists of 10 items on a 4-point scale ranging from "not at all" to "extremely"27. The average score is calculated by dividing the total score by number of items – i.e. ten. Missing values are replaced with the sample mean values for each item. Records with three or more missing items are, however, excluded.

The attendance rates according to previously listed background variables were calculated using data from public registers. The crude and adjusted odds ratios for attendance were estimated in logistic regression models including all the sociodemographic variables as covariates.

To demonstrate the effect of selective attendance on prevalence estimates of selected variables (good/excellent self-reported health, proportion of daily smokers, proportion with obesity (BMI ≥ 30) and proportion with mental distress score 1.85 or above), we used the inverse of the probability of attendance based on logistic regression models as weights. In the first step we weighted with age, sex and education. These variables are known to be strong predictors of health and health behaviour and are commonly used in analyses of social inequality in health. In the second step we included all background variables significantly associated with attendance to see if more adjustments would give a better estimate of the true prevalence. This procedure assumes that the prevalence is similar among the attendees and non-attendees provided they belong to the same sociodemographic categories.

Sensitivity analyses are useful to assess the consequences of dissimilarities (e.g. prevalence) between attendees and non-attendees within each stratum of background variables. We calculated estimates of the "true" prevalence assuming that the prevalence was 10 %, 25 %, 50 % and 100 % higher in non-attendees compared to attendees. Variables with low, medium and high prevalence (diabetes, obesity, symptoms of mental distress, daily smoking and good/excellent health) were included in this analysis.

The possible effects of selected attendance on associations were also assessed based on data from the public registers. Odds ratios for the four exposure variables; education, residential region, country of birth and marital status – and the outcome variable disability benefit, were estimated separately for attendees, non-attendees and for the total invited population. Testing for interactions was done by logistic regression.

Compared to the invited population, the following sub-groups were under-represented among the attendees: males, young age-group, unmarried and separated/divorced, those not born in Norway, inner city dwellers, those with unknown or lower secondary education, low income groups and receivers of disability benefit (Table 2). The other social security benefits, i.e. single parent, rehabilitation and sickness, were not significantly associated with attendance, and are not included in further analyses. The odds ratios of attendance according to country of birth turned non-significant in a model with all covariates included. There were, on the other hand, significant interactions between age, education and country of birth: In the two youngest age groups with lower or upper secondary education the attendance was higher among people born in non-western countries compared to people born in Norway. In the other sub groups of age and education, the attendance was higher among people born in Norway (not shown).

The prevalence estimates of the four selected variables, crude and weighted, are shown in Table 3. The prevalence of good/excellent self-rated health, daily smokers, obesity and symptoms of mental distress, changed only marginally when weighted according to the response model based on age, sex and education (model 1). Similar results were achieved in a response model which also included marital status, residential region, total income and disability benefit (model 2). Response models including interactions between country of birth, education and age did not change these results (data not shown). When we included the oldest age group and adjusted according to the response model 2 without disability benefit, we received similar results (data not shown).

The weighting in table 3 is based on the assumption that attendees and non-attendees are similar within each stratum of sociodemographic variables. Sensitivity analyses are useful if one doubts this similarity. Thus we made different assumptions about prevalence among non-attendees compared to attendees. In Table 4 different examples are shown for variables with low, medium and high prevalence. If the percentage reporting diabetes among non-attending men 30–76 years old is 25 % higher than among the attending men, the true prevalence is 4.3 % compared to 3.8 % in those attending. A 50 % higher diabetes prevalence among the non-attendees implies that the true prevalence will be 4.9 %. Similar calculations for the other four variables showed only moderate changes of the prevalence figures in the target population compared to the attendees if the non-attendees do not differ from the attendees by more than 50%. With more substantial differences between non-attendees and attendees, in the region of a ratio of 2, the true prevalence in the population will be around 50% higher than the prevalence estimated from the attendees only.

A higher percentage of non-attendees received disability benefit compared to people attending – independent of level of education (Table 5). Equal odds ratios appeared among attendees, non-attendees and the total invited population, when analysing the outcome measure disability benefit and the exposure variable level of education. This indicates no selection bias. Performing the same analysis for residential regions and marital status as exposure variables gave similar results – the associations were similar among the invited and the attending population (data not shown).

The pattern changed, however, when country of birth was used as an exposure variable (Table 6). The percentage of individuals born in Norway receiving disability benefit was higher among non-attendees than among attendees, with a maximum ratio of 2 in some sub-groups, but for those born in non-western countries the difference between attendees and non-attendees was much smaller. The ethnic differences were more pronounced in men. The odds ratios of disability benefit comparing non-western and Norwegian born attendees, were higher than the odds ratios among all invited from these countries (interaction between attendance and country of birth in logistic regression, p = 0.003) (Table 6). Thus the difference between individuals born in Norway and in non-western countries was overestimated in analyses of the attendees only, indicating possible selection bias with a 57% over-estimation of OR in men aged 40 and 45 years and 33% in men aged 59–60 years.