Study population
This study was based on the nationally representative Health 2000 Survey, which was carried out in Finland between August 2000 and June 2001 [26, 27]. The population sample of Finnish adults aged 30 or over was formed using a two-stage cluster sampling method [28]. Mainland Finland was divided into five geographical strata based on university hospital districts. In the first stage of sampling, 80 health centre districts (clusters) were selected, and the second stage involved individuals from these districts. The survey had several phases, including many questionnaires, an extensive face-to-face home interview, laboratory and functional capacity tests, and a clinical examination.
We used Vitalograph bellow spirometers (Vitalograph Ltd., Buckingham, UK) to measure lung function. We recorded FEV1 and FVC, using the highest readings from at least two technically valid measurements, in accordance with the guidelines [29]. Pulmonary function varies with age, standing height, sex and ethnicity. Therefore, test results need to be compared to the predicted values and lower limits of normal (LLN). We used the global GLI2012 reference values [2] and defined obstruction as having an FEV1/FVC below LLN, and restriction as having an FVC below LLN. We used the baseline values, because the bronchodilatation test was performed on only part of the study population.
The severity of airflow obstruction was determined on the basis of FEV1% of predicted using the Global Initiative for Chronic Obstructive Lung Disease criteria [4].
A total of 8028 people were sampled, but 51 died before the data was collected. The final sample included 7977 participants, of whom 6986 (88%) were interviewed and 6354 (80%) participated in a health examination [28]. Our study population consisted of those 3386 participants who at the time of baseline examination in 2000–2001 were 1) 30 to 63 years old and 2) full- or part-time employed, and 3) participated in the health examination, including spirometry. We did not include older participants, because the normal retirement age in Finland is 63, and after this it is no longer possible to obtain disability pension. The non-participants in the clinical examinations (n = 385) were slightly younger (mean age 42.8 years vs. 44.4 years), more often male (59.2% vs 49.3%), current smokers (40.5% vs 31.5%), and had physician-diagnosed asthma less frequently (6.0% vs 6.6%) than the participants (n = 3447).
Study groups
We first divided the study population into three groups based on the spirometry: the Obstructive spirometry group was defined as having an FEV1/FVC under LLN in the pre-bronchodilator spirometry. No specific criteria were required for FVC or FEV1. The Restrictive spirometry group was defined as having an FVC under LLN and an FEV1/FVC ≥ LLN in the pre-bronchodilator spirometry. The Controls were defined as having no obstruction and no restriction in spirometry, i.e. FEV1/FVC ≥ LLN and FVC ≥ LLN in pre-bronchodilator spirometry.
We then studied the whole population using the spirometry parameters as continuous variables.
Disability pensions
The Finnish Centre for Pensions Register provided complete information on all retirement events and their primary and secondary diagnoses granted by the independent pension institutions. All pensions granted before December 31, 2011 were linked to the Health 2000 data by each participant’s personal identification number. The follow-up time of retirement events began when a participant completed the questionnaire and ended when one of the following occurred: 1) retirement due to disability pension, 2) retirement due to other reasons (for example age or unemployment), 3) the end of the follow-up period (December, 312,011), or 4) death.
In Finland, a person with a physician-verified chronic illness, disability, or injury, which has been evaluated as causing considerably reduced work ability, is entitled to a part-time or full-time disability pension [30]. The main outcome of this study was retirement due to disability pension, including permanent, temporary, and part-time disability pensions, as well as ‘individual early retirement pension’, which was available until 2005 for employees born before 1944 who had a long working career and whose work ability was substantially reduced, but who did not fulfil the criteria for disability pension. The primary and secondary diagnosis of disability pension were registered and coded on the basis of the International Classification of Diseases and Related Health Problems 10.
Covariates
Detailed information on the variables is described elsewhere [28]. The parameters described here are based on the questionnaire data, unless otherwise mentioned.
Education. Participants who had completed only comprehensive school or part of high school were classified as having a basic education. Those who had completed vocational school or high-school were classified as having a mid-level education. Those who had completed college or who had some other upper secondary or university degree were classified as having a university-level education.
Asthma was defined as the participant reporting having doctor-diagnosed asthma [31]. Self-reported COPD was defined as the participant reporting having COPD.
Smoking. Participants who had not smoked regularly for at least one year were classified as non-smokers. Ex-smokers had smoked for at least one year and quit at least one month earlier. Participants who currently smoked were classified as current smokers.
Cotinine was determined from serum samples collected at baseline and stored at − 20 C°. The method used to determine cotinine concentrations was a modification of the Nicotine Metabolite RIA kit (Diagnostic Products Corporation, LA, USA). For serum cotinine, a high cut-off point of 100 μg was used to separate smokers from non-smokers, as earlier [27].
Body mass index (BMI) was based on measured weight and height.
Other chronic diseases were defined as having one or more of the following: heart disease (ischemic heart disease/heart insufficiency/heart arrhythmia), stroke, rheumatoid arthritis, chronic low back or neck syndrome, a mental disorder, diabetes, or cancer.
Statistical analyses
In our preliminary study, we presented the descriptive statistics for participants in three groups as percentages or mean values with standard deviations (SD). After this preliminary study, we fit Cox proportional hazards regression models to the SAS software package (version 9.2; SAS Institute, Inc., Cary, North Carolina). The dependent variable was the first occurrence of any disability pension from 2000 to 2011. Hazard ratios (HR) and confidence intervals (95% CI) were calculated to estimate the effect of the determinants on whether disability pension was awarded, and were adjusted for covariates. We formed a combined variable with the following categories: 1) obstructive spirometry without or with restriction, 2) restrictive spirometry, no obstruction, 3) no obstruction and no restriction in spirometry. We used this categorised variable as an independent variable in the models. The last category was used as a reference category. These analyses consisted of a crude model and five other models using the following independent covariates: 1) age and gender, 2) education and BMI, 3) one comorbidity and two or more comorbidities, 4) all the aforementioned, and 5) all the aforementioned and current or previous smoking and serum cotinine of > 100 μg. We added smoking-related parameters to the model last, because smoking associates closely with obstructive spirometry.
We used Cox proportional hazards regression models with the same adjusting variables as mentioned above, and divided FEV1/FVC% of predicted, FEV1% of predicted and FVC% of predicted into quartiles (in decreasing order into four groups with an equal number of subjects in each). These groups’ risks of disability pension were compared, using the same adjustments. We finally used Cox proportional hazards regression models with the same adjusting variables, and FEV1/FVC% of predicted, FEV1% of predicted and FVC% of predicted as continuous variables, divided into quartiles in the risk analysis of disability pension.