David S. Fink

Mental Health Among Reserve Component Military Service Members and Veterans

Since 2001, the US military has increasingly relied on National Guard and reserve component forces to meet operational demands. Differences in preparation and military engagement experiences between active component and reserve component forces have long suggested that the psychiatric consequences of military engagement differ by component. We conducted a systematic review of prevalence and new onset of psychiatric disorders among reserve component forces and a meta-analysis of prevalence estimates comparing reserve component and active component forces, and we documented stage-sequential drivers of psychiatric burden among reserve component forces. We identified 27 reports from 19 unique samples published between 1985 and 2012: 9 studies reporting on the reserve component alone and 10 reporting on both the reserve component and the active component. The pooled prevalence for alcohol use disorders of 14.5% (95% confidence interval: 12.7, 15.2) among the reserve component was higher than that of 11.7% (95% confidence interval: 10.9, 12.6) among the active component, while there were no component differences for depression or post-traumatic stress disorder. We observed substantial heterogeneity in prevalence estimates reported by the reserve component. Published studies suggest that stage-sequential risk factors throughout the deployment cycle predicted alcohol use disorders, post-traumatic stress disorder and, to a lesser degree, depression. Improved and more standardized documentation of the mental health burden, as well as study of explanatory factors within a life-course framework, is necessary to inform mitigating strategies and to reduce psychiatric burden among reserve component forces.

Factors Associated With Physical Aggression Among US Army Soldiers

There are a growing number of studies that have approximated levels of aggression and associated outcomes among combat veterans returning from Iraq and Afghanistan using brief screening assessments. However, further research to evaluate the relative role of combat exposures and overt physical behaviors is required to further elucidate potential associations between military service, combat deployment, and overt physical aggression. The purpose of the current study was to assess the prevalence of self-reported physical aggression in a sample of US Army soldiers using an adaptation of the Revised Conflict Tactics Scale (CTS2), and examine factors associated with higher levels of aggression. A population-based cross-sectional study was conducted at a single US Army Installation within a sample of active duty US Army soldiers (n = 6,128) from two large units. Anonymous surveys were collected 6 months following deployment to measure overt aggressive behaviors, posttraumatic stress disorder, anxiety, depression, traumatic brain injury, and misuse of alcohol. There were a relatively higher number of minor and severe physical overt aggressive actions reported among soldiers who previously deployed, notably highest among deployed soldiers reporting the highest levels of combat intensity. Soldiers screening positive for the misuse of alcohol were also significantly more likely to report relatively higher levels of physical aggression. This study quantified overt aggressive behaviors and associated factors, showing increasing combat exposures may result in increased physical aggression. Clinicians treating service members returning from combat may consider assessing relative levels of combat.

The association between combat exposure and negative behavioral and psychiatric conditions

Abstract This study evaluated the association between cumulative combat exposures and negative behavioral and psychiatric conditions. A total of 6128 active-duty soldiers completed a survey approximately 6 months after their unit’s most recent combat deployment. The soldiers self-reported combat exposures and behavioral and psychiatric conditions. Multivariable logistic regression was used to assess the association between cumulative combat exposures and behavioral and psychiatric outcomes. In comparison with the referent group of soldiers not previously deployed, the soldiers categorized as having the highest cumulative combat exposures were significantly associated with self-reporting a history of behavioral and psychiatric diagnoses, problematic alcohol misuse, aggression, criminal behavior, and physical altercations with a significant other. The magnitude and the consistency of the association among the soldiers with the highest number of combat exposures suggest that the number of cumulative combat deployment exposures is an important consideration for identifying and treating high-risk soldiers and units returning from combat.

Life course epidemiology of trauma and related psychopathology in civilian populations.

Traumatic events are ubiquitous exposures that interact with life course events to increase risk of acute psychopathology and alter mental health trajectories. While the majority of persons exposed to trauma experience mild to moderate psychological distress followed by a return to pre-trauma health, many persons exposed to trauma experience substantial distress that lasts for several years. Therefore, in an effort to understand why exposure to trauma can provoke such a range of reactions, we apply a life course approach that considers the complex accumulation and interaction of life experiences that range from social to biological factors, which occur over the life span—from gestation to death and across generations. We present this evidence in three categories: genetics and biology, individual exposures, and community experiences, followed by discussing challenges in existing research and directions for future study.

Patterns of major depression and nonmedical use of prescription opioids in the United States

INTRODUCTION Recent epidemiologic studies have shown that nonmedical use of prescription opioids (NMUPO) and major depression frequently co-occur. Comorbid forms of drug use and mental illness such as NMUPO and depression pose a greater disease burden than either condition alone. However, sociodemographic and substance use differences between individuals with either NMUPO or depression and those with comorbid conditions have not yet been fully investigated. METHODS Data came from the 2011 and 2012 National Survey on Drug Use and Health (NSDUH). Adolescents and adults were examined independently because of differences in screening for major depressive episodes (MDE). Weighted multinomial logistic regression investigated differences between persons with either past-year NMUPO (4.0%) or MDE (5.5%) and those with comorbid NMUPO and MDE (0.6%), compared to persons with neither condition. RESULTS Females were more likely than males to report either MDE-alone and comorbid NMUPO and MDE, whereas adult men were marginally more likely to report NMUPO-alone (not significant among adolescents). Polydrug use and alcohol use disorders were more pronounced among those with comorbid NMUPO and MDE than persons with either NMUPO-alone or MDE-alone. Persons with independent and comorbid NMUPO and MDE were more likely to report lower income and unemployment versus employment. CONCLUSIONS This study found that independent and comorbid NMUPO and MDE were disproportionately clustered with burdens of lower socioeconomic position, suggesting that a population-based approach to address NMUPO would target these social determinants of health, whereas a high-risk approach to prevention should be tailored to females experiencing MDE symptoms and polydrug users.

Association Between Prescription Drug Monitoring Programs and Nonfatal and Fatal Drug Overdoses: A Systematic Review

The overuse of prescription opioids during the past 2 decades has evolved into a major public health issue in the United States. Opioid prescribing increased 350% between 1999 and 2015, from 180 to 640 morphine milligram equivalents per capita (1), with parallel increases in nonmedical use (2, 3), neonatal abstinence syndrome (4), and deaths due to both prescription opioid and heroin overdose (5, 6). The age-adjusted rate of prescription opioidrelated deaths rose from 1.0 to 4.4 deaths per 100000 population between 1999 and 2016, whereas heroin-related deaths increased nearly 5-fold since 2010, rising from 1.0 to 4.9 deaths per 100000 population between 2010 and 2016 (7). State prescription drug monitoring programs (PDMPs) have been advanced as a critical tool to better inform clinical care, identify illegal prescribing, and reduce prescription opioidrelated morbidity and mortality (8, 9). By 2017, all 50 states and the District of Columbia had an operational PDMP or passed legislation to operate a PDMP. Although PDMPs in the United States have commonalities in terms of centralized statewide data systems that electronically transmit prescription data, the administrative features of PDMPs have varied substantially among states and over time. Programs operate under different regulatory agencies, collect different types of data, require data to be updated at different intervals, and allow access to different groups of people. Despite this variability in PDMP administrative features, previous studies found implementation of these programs to be associated with reductions in the supply (10), diversion (11), and misuse of prescription opioids (12). As such, PDMPs are increasingly promoted as valuable, user-friendly, accurate, and real-time digital resources for providers and law enforcement alike (13, 14). However, evidence for the effect of PDMPs on drug-induced overdoses remains unclear. The objective of our review was to systematically search and review the literature to assess whether PDMPs are associated with changes in nonfatal or fatal overdoses; to evaluate whether specific administrative features of PDMPs are differentially associated with these outcomes and, if so, which features are most influential; and to investigate any potential unintended consequences associated with PDMPs. Methods Data Sources and Searches We followed a predefined protocol developed in November 2016 (Supplement 1 and structured reporting of the review according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines (15). We searched 5 online databases (MEDLINE, Current Contents Connect [Clarivate Analytics], Science Citation Index [Clarivate Analytics], Social Sciences Citation Index [Clarivate Analytics], and ProQuest Dissertations) for titles and abstracts of articles that examined an association between PDMP implementation and nonfatal or fatal drug overdoses. We did not impose a time or language restriction on searches (that is, queries surveyed the entire history of each online database). We included dissertations and peer-reviewed articles, as well as both published and in-process texts. We also examined references from the selected materials to identify additional articles and searched ClinicalTrials.gov. The search was first conducted in November 2016 and repeated in December 2017. All the resulting study titles and abstracts were exported to Covidence, a Web interface developed by Cochrane to systematize the review process (16). For the search terms and algorithm used in the literature search, see Appendix Table 1. Supplement. Supplementary Material Appendix Table 1. Search Strategy Study Selection All titles and abstracts were independently screened by 1 of 3 investigators (D.S.F., J.P.S., or K.K.G.) for eligibility, and those considered relevant by any investigator advanced to the full-text review. We included observational studies published in English if they estimated the before-and-after change in rates of nonfatal or fatal drug overdoses after a PDMP was implemented within a single U.S. state or in a set of states. No restrictions were placed on sample size or population age. A PDMP was considered implemented when a state operationalized its program and began to collect and distribute data or to make the data available to authorized users. Data Extraction and Quality Assessment Two researchers (J.P.S. and K.K.G.) independently read selected articles. Using a standardized article assessment form, they captured data on the specific policy studied; outcome data sources; study design; and results, including point estimates and CIs or P values. After the data were abstracted independently from each study, the 2 researchers reviewed the data for each article to ensure consistency and resolve differences. Disagreements between the researchers were reconciled by the first author (D.S.F.). Finally, 2 investigators independently assessed risk of bias (ROB) for the overdose outcomes reported in each study by using the Cochrane Risk Of Bias In Non-randomized Studiesof Interventions (ROBINS-I) assessment tool (17). By answering questions provided by ROBINS-I, the investigators assessed ROB within 8 specific bias domains (confounding, selection of participants, classification, deviations from intended interventions, missing data, measurement of outcomes, selection of the reported results, and overall bias), grading each domain as low, moderate, serious, or critical. Disagreements were resolved by consensus. Data Synthesis and Analysis Because of substantial heterogeneity in the policies examined and the analytic methods applied, we did not do a meta-analysis. Instead, we performed a qualitative assessment and synthesis using methods outlined by the Agency for Healthcare Research and Quality (18). We categorized studies into 5 groups: PDMP implementation only, specific administrative features only, both PDMP implementation and specific administrative features, PDMP implementation with other opioid policies, and PDMP robustness. Studies examining only PDMP implementation treated all PDMPs as homogenous programs without considering how their administrative features have varied among states and over time. Studies investigating specific administrative features compared states with a PDMP having a specific feature (such as mandatory registration or use, frequency of reporting, or proactive reporting) with states that either had no PDMP or had a PDMP without the specific feature. Studies of PDMPs implemented with other, associated opioid policies examined the contribution of PDMP features to those policies. Finally, studies examining PDMP robustness presented quantitative ratings of PDMP features according to their potential effectiveness in reducing diversion and overdose. We also examined 3 outcomes: nonfatal overdoses, fatal overdoses, and unintended consequences. The investigators assessed the overall strength of evidence (SOE), considering 5 domains: study limitations (determined by using ROBINS-I), directness (whether evidence linked interventions directly to a key question in the review), consistency (degree to which studies found the same direction of effect estimates), precision (degree of certainty surrounding an effect estimate), and reporting bias (selective publishing or reporting of findings on the basis of favorability of the direction or magnitude of effect estimates). On the basis of grades from the 5 specific domains, we rated the overall SOE for each intervention and outcome as insufficient, low, moderate, or high. Role of the Funding Source The National Institute on Drug Abuse (NIDA) and Bureau of Justice Assistance (BJA) had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Results Figure 1 depicts the literature search and selection process. Seventeen articles met the inclusion criteria; 4 reported nonfatal drug overdoses, and 13 reported fatal drug overdoses. All were published between 2011 and 2018. Three were doctoral dissertations (1921), and 14 were published in peer-reviewed journals (2235). Of note, outcome data from 1 study were extracted from 2 publications (29, 36). Supplement 2 presents the characteristics and Appendix Table 2 the ROB assessments of the studies. Figure 1. Evidence search and selection. PDMP= prescription drug monitoring program. Appendix Table 2. ROB Assessment in Studies That Reported on the Association Between PDMPs and Nonfatal and Fatal Drug Overdoses The Table shows the various PDMP configurations evaluated in the 17 studies. Of these studies, 8 examined PDMP implementation in general (21, 29, 3035), 2 looked at program features alone (23, 24), 5 analyzed both PDMP implementation and program features (19, 20, 22, 27, 28), 1 investigated PDMP implementation with mandated provider review combined with pain clinic laws (25), and 1 assessed PDMP robustness (26). The study that examined robustness generated a score of PDMP administrative strength or robustness by assigning weights to specific administrative features on the basis of extant evidence, or expert judgment if evidence was lacking, regarding the expected effect of the characteristic on prescribing or overdose, then summing the weights for a PDMP in a given state for a particular year (26). Among the 7 studies that examined program features, whether alone (22, 24) or in addition to PDMPs in general (19, 20, 22, 27, 28), mandatory provider use of or registration for the PDMP was the most frequently evaluated administrative feature, with 1 study examining the association with nonfatal overdoses (28), 4 studies investigating the association with fatal overdoses (20, 22, 24, 27), and 1 study looking at the association with both nonfatal and fatal overdoses (23). In addition, 2 studies examined state authorization for providers to access PDMP data (20, 22), 2 focused on proactive repo

Medical marijuana laws and adolescent marijuana use in the United States: a systematic review and meta-analysis

Abstract Aims To conduct a systematic review and meta‐analysis of studies in order to estimate the effect of US medical marijuana laws (MMLs) on past‐month marijuana use prevalence among adolescents. Methods A total of 2999 papers from 17 literature sources were screened systematically. Eleven studies, developed from four ongoing large national surveys, were meta‐analyzed. Estimates of MML effects on any past‐month marijuana use prevalence from included studies were obtained from comparisons of pre–post MML changes in MML states to changes in non‐MML states over comparable time‐periods. These estimates were standardized and entered into a meta‐analysis model with fixed‐effects for each study. Heterogeneity among the study estimates by national data survey was tested with an omnibus F‐test. Estimates of effects on additional marijuana outcomes, of MML provisions (e.g. dispensaries) and among demographic subgroups were abstracted and summarized. Key methodological and modeling characteristics were also described. Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines were followed. Results None of the 11 studies found significant estimates of pre–post MML changes compared with contemporaneous changes in non‐MML states for marijuana use prevalence among adolescents. The meta‐analysis yielded a non‐significant pooled estimate (standardized mean difference) of −0.003 (95% confidence interval = −0.012, +0.007). Four studies compared MML with non‐MML states on pre‐MML differences and all found higher rates of past‐month marijuana use in MML states pre‐MML passage. Additional tests of specific MML provisions, of MML effects on additional marijuana outcomes and among subgroups generally yielded non‐significant results, although limited heterogeneity may warrant further study. Conclusions Synthesis of the current evidence does not support the hypothesis that US medical marijuana laws (MMLs) until 2014 have led to increases in adolescent marijuana use prevalence. Limited heterogeneity exists among estimates of effects of MMLs on other patterns of marijuana use, of effects within particular population subgroups and of effects of specific MML provisions.

Increase in suicides the months after the death of Robin Williams in the US

Investigating suicides following the death of Robin Williams, a beloved actor and comedian, on August 11th, 2014, we used time-series analysis to estimate the expected number of suicides during the months following Williams’ death. Monthly suicide count data in the US (1999–2015) were from the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER). Expected suicides were calculated using a seasonal autoregressive integrated moving averages model to account for both the seasonal patterns and autoregression. Time-series models indicated that we would expect 16,849 suicides from August to December 2014; however, we observed 18,690 suicides in that period, suggesting an excess of 1,841 cases (9.85% increase). Although excess suicides were observed across gender and age groups, males and persons aged 30–44 had the greatest increase in excess suicide events. This study documents associations between Robin Williams’ death and suicide deaths in the population thereafter.

Trajectories of posttraumatic stress symptoms after civilian or deployment traumatic event experiences.

Objective: Growth mixture model studies have observed substantial differences in the longitudinal patterns of posttraumatic stress symptom (PTSS) trajectories. This variability could represent chance iterations of some prototypical trajectories or measurable variability induced by some aspect of the source population or traumatic event experience. Testing the latter, the authors analyzed a nationally representative sample of U.S. Reserve and National Guard members to identify the influence of civilian versus deployment trauma on the number of PTSS trajectories, the nature of these trajectories, and the proportion of respondents in each trajectory. Method: Data were collected from 2010 to 2013 and latent class growth analysis was used to identify different patterns of PTSS in persons exposed to both a civilian and a deployment trauma and to test whether respondents’ exposure to civilian trauma developed similar or distinct patterns of response compared to respondents exposed to deployment trauma. Results: PTSS were found to follow 3 trajectories, with respondents predominantly clustered in the lowest symptom trajectory for both trauma types. Covariates associated with each trajectory were similar between the 2 traumas, except number of civilian-related traumatic events; specifically, a higher number of civilian traumatic events was associated with membership in the borderline-stable, compared to low-consistent, trajectory, for civilian traumas and associated with the preexisting chronic trajectory for military traumas. Conclusions: Holding the source population constant, PTSS trajectory models were similar for civilian and deployment-related trauma, suggesting that irrespective of traumatic event experienced there might be some universal trajectory patterns. Thus, the differences in source populations may have induced the heterogeneity observed among prior PTSS trajectory studies.

Health insurance, alcohol and tobacco use among pregnant and non-pregnant women of reproductive age.

BACKGROUND Understanding the relationship between health insurance coverage and tobacco and alcohol use among reproductive age women can provide important insight into the role of access to care in preventing tobacco and alcohol use among pregnant women and women planning to become pregnant. METHODS We examined the association between health insurance coverage and both past month alcohol use and past month tobacco use in a nationally representative sample of women age 12-44 years old, by pregnancy status. The women (n=97,788) were participants in the National Survey of Drug Use and Health (NSDUH) in 2010-2013. Logistic regression models assessed the association between health insurance (insured versus uninsured), past month tobacco and alcohol use, and whether this was modified by pregnancy status. RESULTS Pregnancy status significantly moderated the relationship between health insurance and tobacco use (p-value≤0.01) and alcohol use (p-value≤0.01). Among pregnant women, being insured was associated with lower odds of alcohol use (adjusted odds ratio [AOR]=0.47; 95% confidence interval [CI]=0.27-0.82), but not associated with tobacco use (AOR=1.14; 95% CI=0.73-1.76). Among non-pregnant women, being insured was associated with lower odds of tobacco use (AOR=0.67; 95% CI=0.63-0.72), but higher odds of alcohol use (AOR=1.23; 95% CI=1.15-1.32). CONCLUSION Access to health care, via health insurance coverage is a promising method to help reduce alcohol use during pregnancy. However, despite health insurance coverage, tobacco use persists during pregnancy, suggesting missed opportunities for prevention during prenatal visits.