No abstract

BackgroundThe association between mental distress and divorce is well established in the literature. Explanations are commonly classified within two different frameworks; social selection (mentally distressed people are selected out of marriage) and social causation (divorce causes mental distress). Despite a relatively large body of literature on this subject, selection effects are somewhat less studied, and research based on data from both spouses is scarce. The purpose of the present study is to investigate selection effects both at the individual level and the couple level.MethodsThe current study is based on couple-level data from a Norwegian representative sample including 20,233 couples. Long-term selection effects were tested for by means of Cox proportional hazard models, using mental distress in both partners at baseline as predictors of divorce the next 16 years. Three identical sets of analyses were run. The first included the total sample, whereas the second and third excluded couples who divorced within the first 4 or 8 years after baseline, respectively. An interaction term between mental distress in husband and in wife was specified and tested.ResultsHazard of divorce was significantly higher in couples with one mentally distressed partner than in couples with no mental distress in all analyses. There was also a significant interaction effect showing that the hazard of divorce for couples with two mentally distressed partners was higher than for couples with one mentally distressed partner, but lower than what could be expected from the combined main effects of two mentally distressed partners.ConclusionsOur results suggest that mentally distressed individuals are selected out of marriage. We also found support for a couple-level effect in which spouse similarity in mental distress to a certain degree seems to protect against divorce.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-1662-0) contains supplementary material, which is available to authorized users.

Background Health researchers often use survey studies to examine associations between risk factors at one time point and health outcomes later in life. Previous studies have shown that missing not at random (MNAR) may produce biased estimates in such studies. Medical researchers typically do not employ statistical methods for treating MNAR. Hence, there is a need to increase knowledge about how to prevent occurrence of such bias in the first place. Methods Monte Carlo simulations were used to examine the degree to which selective non-response leads to biased estimates of associations between risk factors and health outcomes when persons with the highest levels of health problems are under-represented or totally missing from the sample. This was examined under different response rates and different degrees of dependency between non-response and study variables. Results Response rate per se had little effect on bias. When extreme values on the health outcome were completely missing, rather than under-represented, results were heavily biased even at a 70% response rate. In most situations, 50–100% of this bias could be prevented by including some persons with extreme scores on the health outcome in the sample, even when these persons were under-represented. When some extreme scores were present, estimates of associations were unbiased in several situations, only mildly biased in other situations, and became biased only when non-response was related to both risk factor and health outcome to substantial degrees. Conclusions The potential for preventing bias by including some extreme scorers in the sample is high (50–100% in many scenarios). Estimates may then be relatively unbiased in many situations, also at low response rates. Hence, researchers should prioritize to spend their resources on recruiting and retaining at least some individuals with extreme levels of health problems, rather than to obtain very high response rates from people who typically respond to survey studies. This may contribute to preventing bias due to selective non-response in longitudinal studies of risk factors and health outcomes.

No abstract

No abstract