Niels Henrik Hjøllund1, Finn Breinholt Larsen2, Johan Hviid Andersen3
1Department of Clinical Social Medicine, Institute of Public Health, Aarhus University, Denmark, Center of Public Health, Region Mid-Jutland, Aarhus, Denmark,
2Center of Public Health, Region Mid-Jutland, Aarhus, Denmark
3Department of Occupational Medicine, Herning Hospital, Herning, Denmark
Tóm tắt
Background: Social consequences of disease may be subject to register based follow-up. A Danish database, DREAM, allows weekly follow-up of any public transfer payment. This study aimed to evaluate the feasibility of the register for use in public health research. Material and methods: The DREAM database includes information on all public transfer payments administered by Danish ministries, municipalities, and Statistics Denmark for all Danish citizens on a weekly basis since 1991. The DREAM database was compared with self-reported information on sources of income in a population survey from 2001 with about 5,000 participants. Results: According to DREAM, 80.2% of respondents had received some kind of transfer income since 1991. For the week they filled in the questionnaire, 9.0% had a record of labour-market-related benefit (unemployment benefit, social assistance, wage subsidy), 6.4% a health-related benefit (sickness benefit, vocational rehabilitation allowance, salary from subsidized jobs for persons with limited work capacity, anticipatory pension), 10.1% a voluntary retirement pension, while 74.4% had no record of transfer payment for that week. The predictive value of DREAM was 74.8% for health-related transfer payment and 98.2% for self-support. Among persons with a record of sickness benefit, 52.4% reported no transfer payment. Conclusion: The DREAM database is feasible for follow-up of social and economic consequences of disease. Respondents may be unaware of payments transferred by the public authorities to the employer, and in such cases DREAM may be the best source of information. The database is useful for public health research, but may also be useful for socioeconomic analyses of selection bias and dropout from other studies.
