Register-based research in the Nordic countries

1. Register-based research in the Nordic countries Mika Gissler Nordic School of Public Health, Gothenburg, Sweden & THL National Institute for Health and Welfare, Helsinki, Finland

2. Why good possibilities to register-based studies? • Traditions: population statistics have been collected more than 250 years and health statistics more than 150 years in the Nordic countries. • First real registers were started in the 1940-1950s, when improved computers were available: health care personnel, cancer register. • Personal identification numbers since 1960s. • Several data quality studies have shown the high quality of routinely collected registers. • Data protection allows research use of register data.

3. Important registers in the Nordic countries • Cancer register 1940s • Registers on infectious diseases 1950s • Hospital discharge registers 1960s • Cause-of-death registers 1960s • Birth and malformation registers 1960s • Register-based Census 1990s • Health care quality registers 1990s • Prescription registers 1990s • Hospital outpatient registers 1990s

4. Unique registers and data in the Nordic countries • IVF (in vitro fertilization) register, Denmark • Register on induced abortions and sterilisations, Finland • Register on visual impairments, Finland • Register on breast and cervical cancer screening, Finland • Multiple generation register, Sweden • Multiple generation studies in the Norwegian Medical Birth Register • Biobanks in all Nordic countries + possibilities to link them to other registers.

5. Important registers for studies in psychiatry and mental health • Hospital discharge registers 1960s • Cause-of-death registers 1960s • Pension Registers 1960s • Register-based Census 1990s • Prescription registers 1990s • Hospital outpatient registers 1990s

6. Examples of register-based studies in psychiatry and mental health • Register-based studies: • Cross-sectional studies • Trends • Longitudinal studies • Combination of data from different sources: • Medical records • Questionnaires • Biobank material

7. Example 1: Life expectancy among psychiatric patients • Registers: • THL: Hospital Discharge Register 1980-2003 • Finnish Centre of Pension: Pension Register 1980-2003 • Statistics Finland: Cause-of-Death Register 1981-2003 • Data: • The data included 361 898 persons aged 15 years or more • 17 638 persons with dementia and 2 630 with intellectual disability were excluded • Life expectancy at 15 years and for ages 15-64 years were calculated separately by using Wiesler's method.

8. Life expectancy increased

9. Conclusions • Life expectancy at 15 years has increased among Finnish population with hospital discharge or pension due to mental disorders between 1981 and 2003: • Finland: +3.5 years, psychiatric patients +5.8 years • F30-39: +10 years, F40-49: +8 years, F20-29: +6 years, but • F10-19: -0.6 years • Risk for death • diseases and medical conditions 2-fold • external causes and poisoning 6-fold • Similar results from other Nordic countries.

10. Example 2: Maternal smoking and children’s F-diagnoses • Registers: • THL: Medical Birth Register 1987-1989 • THL: Hospital Discharge Register 1987-2007 • Social Insurance Institute: Reimbursed psychotropic medicine 1994-2007 • Statistics Finland: Cause-of-Death registers 1987-2008 • Data: • Children born in 1987-1989, excluding perinatal deaths, multiples, and children with major congenital anomalies • Final study population: 175 869 children (94.4%)

11. Risk for adverse psychiatric outcomes by maternal smoking Adjusted by maternal age, parity, sex, gestational age, birth weight, 5 minute Apgar score and maternal psychiatric diagnosis before birth.

12. Conclusions • Children exposed to maternal smoking has an increased risk for receiving a F-diagnosis in inpatient or outpatient care in childhood and adolescent. • The increased risk can be observed for all diagnosis excluding schizophrenia and anorexia. • Register studies cannot confirm the real effect of smoking. • However, a recent local study in Turku has shown that prenatal smoking exposure is associated with smaller regional brain volumes in preterm infants (Ekblad et al., J Pediatrics 2009).

13. Example 3: Use of psychotropic drugs and pregnancy outcomes • Registers: • The ‘Drug and Pregnancy’ -database 1996-2006, to be annually completed 2007 onwards • Data: • All births in the Medical Birth Register • All induced abortions in the Abortion Register • All congenital anomalies in the Malformation Register • Use of prescribed & reimbursed drugs (Social Insurance Institution) • 3 months before pregnancy • during pregnancy • 3 months after pregnancy

14. The use of psychotropic medicine before the pregnancy starts • The Drug and Pregnancy -database 1996-2006: • Total 622 671 births and 117 229 induced abortions • Excluded: induced abortions due to fetal reasons • Separate analysis: first pregnancies • All drug purchases 3 months before pregnancy were used as a proxy measure of mental health disorders.

15. Conclusions • Measured by the use of psychotropic medicine, women’s pre-existing mental health status is worse for women having an induced abortion than for women giving a birth. • All pregnancies: Adjusted OR 1.94 (95% CI 1.87-2.02) • First pregnancies: Adjusted OR 1.56 (95% CI 1.44-1.68) • Highest risk for women using hypnotics and sedatives, antipsychotics and antidepressants. • This essential confounding factor should not be neglected when investigating the occurrence of pregnancy-related mental health problems.

16. Example 4: Mothers’ and children’s long-term follow-up after substance abuse during pregnancy • Basic data: • 524 women followed-up prenatally at special out-patient clinics and a control group of 1792 women matched for maternal age, parity, time and place of delivery. • Registers: • THL: Medical Birth Register, Hospital Discharge Register, Child Welfare Register • Statistics Finland: Cause-of-Death Register • Social Insurance Institution: Information on prescribed medicine, social benefits, pensions and rehabilitations

17. Mothers’ outcome, % Cases Controls • Death 8.0 0.2 *** • F-diagnosis, inpatient 46.0 3.6 *** • F-diagnosis, outpatient 47.1 8.3 *** • Intoxication care 41.3 1.8 *** • Pensions, any cause 16.8 2.2 *** • Rehabilitation, any cause 9.5 5.6 *** • Special reimbursement 27.0 18.4 *** • Psychosis 10.9 1.4 *** • Drug reimbursement N05 71.4 20.9 *** • Drug reimbursement N06 68.1 26.5 ***

18. Children’s outcome, % Cases Controls • Death 1.4 1.0 NS • F-diagnosis, inpatient 7.1 2.8 *** • F-diagnosis, outpatient 8.1 2.6 *** • Care benefit for sick child 25.0 13.9 *** • Rehabilitation, any cause 5.1 2.4 *** • Special reimbursement 12.1 11.1 NS • Drug reimbursement N05 9.1 5.6 ** • Drug reimbursement N06 4.6 1.4 *** • Child taken into custody 46.0 2.4 ***

19. Conclusions • Combination of medical records and registers was feasible, even though it was difficult to get all the necessary permissions. • Women with substance abuse displayed significant long-term abuse-related morbidity and mortality, rehabilitation, early retirement, and use of prescribed medicine. • Also their children had increased morbidity, rehabilitation, and use of prescribed medicine, and almost half of them were taken into custody.

20. Why register research? • Easy to form data: • cross-sectional studies • longitudinal studies (history, follow-up) • Easy to repeat the same study. • No limitations for sample size (rare cases --- total population). • Population-based studies feasible. • No need to contact patients. • Follow-up relatively easy. • No participation bias nor research bias. • No reporting bias.

21. Problems related to register research • The data is unavailable • primary health care, diseases and conditions not requiring a contact to health care system, self-rated health, opinions, experiences,... • Data protection: are such studies possible in general? • Ethically controversial topics: • abortion, miscarriage, infertility, malformations, psychiatric disorders, family studies, contact to relatives of a death patient, genetics… • High data costs: Statistical offices, Central Population Register • Data overload syndrome • Too much data, too little time…? • Fishing: • Easy to find statistically significant results, if the data is large.

22. Finally • Register-based studies seems to be feasible, e.g. for cross-sectional, longitudinal and trend studies • Combination of data from other registers and from other sources, such as medical records, questionnaires and even biobank material is possible. • Data protection questions have not been an issue, at least until now. • The lack of information from primary health care will be solved after the national electronic patient journal system is in use.

23. Promotion of register research • Denmark: National Centre for Register-based Research, Århus Universitet http://www.ncrr.dk/ • Finland: Finnish Information Centre for Register Research http://www.rekisteritutkimus.fi/ • Norway: Special issue on register-based research in Norsk Epidemiologi 14 (1): 2004. • Sweden: Grants for register-based research by the National Board of Welfare and Health (Social-styrelsen)