Background and Aims Most US states have passed medical marijuana laws (MMLs), with great variation in program regulation impacting enrollment rates. We aimed to compare changes in rates of marijuana use, heavy use and cannabis use disorder across age groups while accounting for whether states enacted medicalized (highly regulated) or non-medical mml programs. Design Difference-in-differences estimates with time-varying state-level MML coded by program type (medicalized versus non-medical). Multi-level linear regression models adjusted for state-level random effects and covariates as well as historical trends in use. Setting Nation-wide cross-sectional survey data from the US National Survey of Drug Use and Health (NSDUH) restricted use data portal aggregated at the state level. Participants Participants comprised 2004–13 NSDUH respondents (n ~ 67 500/year); age groups 12–17, 18–25 and 26+ years. States had implemented eight medicalized and 15 non-medical MML programs. Measurements Primary outcome measures included (1) active (past-month) marijuana use; (2) heavy use (> 300 days/year); and (3) cannabis use disorder diagnosis, based on DSM-IV criteria. Covariates included program type, age group and state-level characteristics throughout the study period. Findings Adults 26+ years of age living in states with non-medical MML programs increased past-month marijuana use 1.46% (from 4.13 to 6.59%, P = 0.01), skewing towards greater heavy marijuana by 2.36% (from 14.94 to 17.30, P = 0.09) after MMLs were enacted. However, no associated increase in the prevalence of cannabis use disorder was found during the study period. Our findings do not show increases in prevalence of marijuana use among adults in states with medicalized MML programs. Additionally, there were no increases in adolescent or young adult marijuana outcomes following MML passage, irrespective of program type. Conclusions Non-medical marijuana laws enacted in US states are associated with increased marijuana use, but only among adults aged 26+ years. Researchers and policymakers should consider program regulation and subgroup characteristics (i.e. demographics) when assessing for population level outcomes. Researchers and policymakers should consider program regulation and subgroup characteristics (i.e. demographics) when assessing for population level outcomes.

The death of a loved one is one of life's greatest stressors. Most bereaved individuals experience a period of acute grief that diminishes in intensity as they adapt to the changes brought about by their loss. Over the past four decades a growing body of research has focused on a form of prolonged grief that is painful and impairing. There is a substantial and growing evidence base supports the validity and significance of a grief-related disorder, including the clinical value of being able to diagnose it and provide effective targeted treatment. ICD-11 will include a new diagnosis of Prolonged Grief Disorder (PGD). DSM-5 called this condition Persistent Complex Bereavement Disorder (PCBD) and included it in Section III, signaling agreement that a diagnosis is warranted while further research is needed to determine the optimal criteria. Given the remaining uncertainties, reading this literature can be confusing. There is inconsistency in naming the condition (including complicated grief as well as PGD and PCBD) and lack of uniformity in identifying it, with respect to the optimal threshold and timeframe for distinguishing it from normal grief. As an introductory commentary for this Depression and Anxiety special edition on this form of grief, the authors discuss the history, commonalities, and key areas of variability in

Pregnant women were worried about Zika, yet had gaps in their factual knowledge. Most women reported they would get vaccinated if a vaccine was available. Pregnant women who reported themselves as vulnerable (being worried, having lived in or traveled to a Zika area) were more likely to view behavioral strategies as hard to do and to accept vaccination.

Medical comorbidity is prevalent in Complicated Grief, associated with increasing age and co-occurring depressive symptoms but apparently not with chronicity and severity of Complicated Grief per se. This observation suggests that treating depression in the context of CG may be important to managing medical conditions in individuals with Complicated Grief to attenuate or prevent the long-term medical sequelae of CG.

In psychiatry, clinicians use criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to diagnose mental disorders. Most criteria sets have several symptom domains, and in order to be diagnosed, an individual must meet the minimum number of symptoms required by each domain. Some efforts are now focused on adding biomarkers to these symptom domains to facilitate the detection of and highlight the neurobiological basis of psychiatric disorders. Thus, a new criteria set may consist of both clinical symptom counts in several domains and continuous biomarkers. In this paper, we propose a method to integrate classification rules from multiple data sources to estimate an optimal criteria set. Each domain‐specific rule can be counts of symptoms, a linear function of symptoms, or even nonparametric. The overall classification rule is the intersection of these domain‐specific rules. Based on examining the expected population loss function, we propose two iterative algorithms using either support vector machines or logistic regression to fit intersection rules consistent with the Diagnostic and Statistical Manual of Mental Disorders. In simulation studies, these proposed methods are comparable with the true decision rule. The methods are applied to the motivating study to construct a criteria set for complicated grief. The developed criteria set shows a substantial improvement in sensitivity and specificity compared to the current standards on an independent validation study.