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Recurrence risks for schizophrenia in a Swedish National Cohort

  • PAUL LICHTENSTEIN (a1), CAMILLA BJÖRK (a1), CHRISTINA M. HULTMAN (a1) (a2), EDWARD SCOLNICK (a3), PAMELA SKLAR (a3) and PATRICK F. SULLIVAN (a1) (a4)...

Abstract

Objective. Recurrence risk estimates for schizophrenia are fundamental to our understanding of this complex disease. Widely cited estimates are from small/older samples. If these estimates are biased upwards, then the rationale for molecular genetic studies of schizophrenia may not be as solid.

Method. We created a population-based, Swedish national cohort by linking two Swedish national registers into a relational database (the Swedish Hospital Discharge Register and the Multi-Generation Register). Affection was defined as the lifetime presence of at least two in-patient hospitalizations with a core schizophrenia diagnosis.

Results. Merging the Swedish national registers created a population-based cohort of 7739202 individuals of known parentage. The lifetime prevalence of the narrow definition of schizophrenia was 0·407% and we estimated that one in every 79 extended Swedish families had been impacted by schizophrenia. The proportion of affected families with multiple affected members was 3·81%. Recurrence risk estimates for all relative types were strikingly similar to those reported in smaller and older studies. For example, we estimated λsibs at 8·55 [95% confidence interval (CI) 7·86–9·57] compared with a literature estimate of 8·6.

Conclusions. In the largest and most comprehensive sample yet studied, we confirm the accepted estimates of recurrence risks for schizophrenia, and provide more accurate estimates of recurrence risks of schizophrenia in relatives, an estimate of the familial impact of schizophrenia, and the multiplex proportion (essential for gauging the generalizability of findings from multiplex pedigrees). These data may be valuable for planning and interpreting genetic studies of schizophrenia.

Copyright

Corresponding author

Department of Genetics, CB#7264, 4109D Neurosciences Research Building, University of North Carolina, Chapel Hill, NC 27599-7264, USA. (Email: pfsulliv@med.unc.edu.)

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