Assessing the "healthy immigrant effect" in mental health: Intra- and inter-cohort trends in mood and/or anxiety disorders.

BACKGROUND: The healthy immigrant effect implies that, at the time of immigration, new immigrants are typically healthier than the Canadian-born population. Furthermore, this health advantage fades the longer cohorts of immigrants remain in the host country. METHODS: Most studies assessing the healthy immigrant effect rely on strong, untestable assumptions to extract unique effects for length of stay (LOS) (i.e., how long an immigrant has been in a host country), period (i.e., year of observation), and cohort (i.e., year of immigration). Rather than attempting to parse out separate effects for LOS, period, and cohort, we adopt a descriptive, cohort-centric approach to study immigrant mental health, which examines intra- and inter-cohort trends, that is, joint LOS-period and cohort-period parameters, respectively. While intra-cohort trends show how immigrants' mental health change with LOS across periods, inter-cohort trends reveal how the mental health of successive cohorts of immigrants differ across time periods. To provide a thorough assessment of the healthy immigrant effect, we use both survey and administrative data on cohorts of Canadian immigrants from 2003 to 2013. RESULTS: The survey data reveal that mental health declines steeply (i.e., there is an increase in mood and/or anxiety disorders) within and across immigrant cohorts, while the administrative data show little overall change in mental health care utilization within and across cohorts. The divergent results may reflect issues related to barriers in access to mental health services because the administrative data, which are based on health care utilization, do not the capture the increase in mental disorders seen in the survey data. CONCLUSION: This study highlights the benefit of a cohort-based approach to assess the healthy immigrant effect as it pertains to mental health as well as the importance of using different types of data, which may be measuring different aspects of immigrant mental health and health care utilization.

Bounding Analyses of Age-Period-Cohort Effects.

For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been proposed to address this issue, but they have suffered from either ad hoc assumptions or extreme sensitivity to small differences in model specification. After briefly reviewing past work, we outline a new approach for identifying temporal effects in population-level data. Fundamental to our framework is the recognition that it is only the slopes of an APC model that are unidentified, not the nonlinearities or particular combinations of the linear effects. One can thus use constraints implied by the data along with explicit theoretical claims to bound one or more of the APC effects. Bounds on these parameters may be nearly as informative as point estimates, even with relatively weak assumptions. To demonstrate the usefulness of our approach, we examine temporal effects in prostate cancer incidence and homicide rates. We conclude with a discussion of guidelines for further research on APC effects.