The Million Veteran Program 1990-1991 Gulf War Era Survey: An Evaluation of Veteran Response, Characteristics, and Representativeness of the Gulf War Era Veteran Population.

To address gaps in understanding the pathophysiology of Gulf War Illness (GWI), the VA Million Veteran Program (MVP) developed and implemented a survey to MVP enrollees who served in the U.S. military during the 1990-1991 Persian Gulf War (GW). Eligible Veterans were invited via mail to complete a survey assessing health conditions as well as GW-specific deployment characteristics and exposures. We evaluated the representativeness of this GW-era cohort relative to the broader population by comparing demographic, military, and health characteristics between respondents and non-respondents, as well as with all GW-era Veterans who have used Veterans Health Administration (VHA) services and the full population of U.S. GW-deployed Veterans. A total of 109,976 MVP GW-era Veterans were invited to participate and 45,270 (41%) returned a completed survey. Respondents were 84% male, 72% White, 8% Hispanic, with a mean age of 61.6 years (SD = 8.5). Respondents were more likely to be older, White, married, better educated, slightly healthier, and have higher socioeconomic status than non-respondents, but reported similar medical conditions and comparable health status. Although generally similar to all GW-era Veterans using VHA services and the full population of U.S. GW Veterans, respondents included higher proportions of women and military officers, and were slightly older. In conclusion, sample characteristics of the MVP GW-era cohort can be considered generally representative of the broader GW-era Veteran population. The sample represents the largest research cohort of GW-era Veterans established to date and provides a uniquely valuable resource for conducting in-depth studies to evaluate health conditions affecting 1990-1991 GW-era Veterans.

Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications.

As recreational use of cannabis is being decriminalized in many places and medical use widely sanctioned, there are growing concerns about increases in cannabis use disorder (CanUD), which is associated with numerous medical comorbidities. Here we performed a genome-wide association study of CanUD in the Million Veteran Program (MVP), followed by meta-analysis in 1,054,365 individuals (ncases = 64,314) from four broad ancestries designated by the reference panel used for assignment (European n = 886,025, African n = 123,208, admixed American n = 38,289 and East Asian n = 6,843). Population-specific methods were applied to calculate single nucleotide polymorphism-based heritability within each ancestry. Statistically significant single nucleotide polymorphism-based heritability for CanUD was observed in all but the smallest population (East Asian). We discovered genome-wide significant loci unique to each ancestry: 22 in European, 2 each in African and East Asian, and 1 in admixed American ancestries. A genetically informed causal relationship analysis indicated a possible effect of genetic liability for CanUD on lung cancer risk, suggesting potential unanticipated future medical and psychiatric public health consequences that require further study to disentangle from other known risk factors such as cigarette smoking.

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program.

BACKGROUND: The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic changes over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). To investigate these aspects, we divided MVP participants into five birth cohorts (N-range = 123,888 [born from 1943 to 1947] to 136,699 [born from 1948 to 1953]). RESULTS: Ancestry groups were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP + HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010 < Cohen's d < 0.259, p < 7.80 × 10-4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept = 1.08 ± 0.042). The 1kGP + HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction = 0.045 ± 0.007, p < 0.05) confounding in the GWAS statistics. CONCLUSIONS: This study provides a characterization of ancestry diversity of the MVP cohort over time and compares two strategies to infer genetically defined ancestry groups by assessing differences in controlling population stratification in genome-wide association studies.

Factor structure of the posttraumatic stress disorder checklist (PCL-17) in 279,897 million veteran program participants.

The Million Veteran Program (MVP) uses the posttraumatic stress disorder symptoms (PTSD) Checklist 17 (PCL-17) self-report to assess PTSD. Existing literature suggests that the five-factor dysphoric arousal model best represents the PTSD symptom clusters; this can be tested within MVP, one of the largest samples collected with suitable data. We compared factor models within MVP across genetically defined subsamples (ancestry [European, African, admixed American, and East Asian], sex) via multi-group confirmatory factor analyses in a sample of 279,897 participants. The five-factor dysphoric arousal model best fit the PCL-17 data, consistent with previous findings. The factor structure could also be imposed across all groups tested. Verifying the factor structure provides a framework for future phenotypic and genotypic analyses within MVP and other samples.

African ancestry GWAS of dementia in a large military cohort identifies significant risk loci.

While genome wide association studies (GWASs) of Alzheimer's Disease (AD) in European (EUR) ancestry cohorts have identified approximately 83 potentially independent AD risk loci, progress in non-European populations has lagged. In this study, data from the Million Veteran Program (MVP), a biobank which includes genetic data from more than 650,000 US Veteran participants, was used to examine dementia genetics in an African descent (AFR) cohort. A GWAS of Alzheimer's disease and related dementias (ADRD), an expanded AD phenotype including dementias such as vascular and non-specific dementia that included 4012 cases and 18,435 controls age 60+ in AFR MVP participants was performed. A proxy dementia GWAS based on survey-reported parental AD or dementia (n = 4385 maternal cases, 2256 paternal cases, and 45,970 controls) was also performed. These two GWASs were meta-analyzed, and then subsequently compared and meta-analyzed with the results from a previous AFR AD GWAS from the Alzheimer's Disease Genetics Consortium (ADGC). A meta-analysis of common variants across the MVP ADRD and proxy GWASs yielded GWAS significant associations in the region of APOE (p = 2.48 × 10-101), in ROBO1 (rs11919682, p = 1.63 × 10-8), and RNA RP11-340A13.2 (rs148433063, p = 8.56 × 10-9). The MVP/ADGC meta-analysis yielded additional significant SNPs near known AD risk genes TREM2 (rs73427293, p = 2.95 × 10-9), CD2AP (rs7738720, p = 1.14 × 10-9), and ABCA7 (rs73505251, p = 3.26 × 10-10), although the peak variants observed in these genes differed from those previously reported in EUR and AFR cohorts. Of the genes in or near suggestive or genome-wide significant associated variants, nine (CDA, SH2D5, DCBLD1, EML6, GOPC, ABCA7, ROS1, TMCO4, and TREM2) were differentially expressed in the brains of AD cases and controls. This represents the largest AFR GWAS of AD and dementia, finding non-APOE GWAS-significant common SNPs associated with dementia. Increasing representation of AFR participants is an important priority in genetic studies and may lead to increased insight into AD pathophysiology and reduce health disparities.

Alzheimer's disease and related dementias among aging veterans: Examining gene-by-environment interactions with post-traumatic stress disorder and traumatic brain injury.

INTRODUCTION: Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) confer risk for Alzheimer's disease and related dementias (ADRD). METHODS: This study from the Million Veteran Program (MVP) evaluated the impact of apolipoprotein E (APOE) ε4, PTSD, and TBI on ADRD prevalence in veteran cohorts of European ancestry (EA; n = 11,112 ADRD cases, 170,361 controls) and African ancestry (AA; n = 1443 ADRD cases, 16,191 controls). Additive-scale interactions were estimated using the relative excess risk due to interaction (RERI) statistic. RESULTS: PTSD, TBI, and APOE ε4 showed strong main-effect associations with ADRD. RERI analysis revealed significant additive APOE ε4 interactions with PTSD and TBI in the EA cohort and TBI in the AA cohort. These additive interactions indicate that ADRD prevalence associated with PTSD and TBI increased with the number of inherited APOE ε4 alleles. DISCUSSION: PTSD and TBI history will be an important part of interpreting the results of ADRD genetic testing and doing accurate ADRD risk assessment.

Million Veteran Program's response to COVID-19: Survey development and preliminary findings.

BACKGROUND: In response to the novel Coronavirus Disease 2019 (COVID-19) pandemic, the Department of Veterans Affairs (VA) Million Veteran Program (MVP) organized efforts to better understand the impact of COVID-19 on Veterans by developing and deploying a self-reported survey. METHODS: The MVP COVID-19 Survey was developed to collect COVID-19 specific elements including symptoms, diagnosis, hospitalization, behavioral and psychosocial factors and to augment existing MVP data with longitudinal collection of key domains in physical and mental health. Due to the rapidly evolving nature of the pandemic, a multipronged strategy was implemented to widely disseminate the COVID-19 Survey and capture data using both the online platform and mailings. RESULTS: We limited the findings of this paper to the initial phase of survey dissemination which began in May 2020. A total of 729,625 eligible MVP Veterans were invited to complete version 1 of the COVID-19 Survey. As of October 31, 2020, 58,159 surveys have been returned. The mean and standard deviation (SD) age of responders was 71 (11) years, 8.6% were female, 8.2% were Black, 5.6% were Hispanic, and 446 (0.8%) self-reported a COVID-19 diagnosis. Over 90% of responders reported wearing masks, practicing social distancing, and frequent hand washing. CONCLUSION: The MVP COVID-19 Survey provides a systematic collection of data regarding COVID-19 behaviors among Veterans and represents one of the first large-scale, national surveillance efforts of COVID-19 in the Veteran population. Continued work will examine the overall response to the survey with comparison to available VA health record data.

Association of Gulf War Illness with Characteristics in Deployed vs. Non-Deployed Gulf War Era Veterans in the Cooperative Studies Program 2006/Million Veteran Program 029 Cohort: A Cross-Sectional Analysis.

Gulf War Illness (GWI), a chronic multisymptom illness with a complex and uncertain etiology and pathophysiology, is highly prevalent among veterans deployed to the 1990-1991 GW. We examined how GWI phenotypes varied by demographic and military characteristics among GW-era veterans. Data were from the VA's Cooperative Studies Program 2006/Million Veteran Program (MVP) 029 cohort, Genomics of GWI. From June 2018 to March 2019, 109,976 MVP enrollees (out of a total of over 676,000) were contacted to participate in the 1990-1991 GW-era Survey. Of 109,976 eligible participants, 45,169 (41.1%) responded to the 2018-2019 survey, 35,902 respondents met study inclusion criteria, 13,107 deployed to the GW theater. GWI phenotypes were derived from Kansas (KS) and Centers for Disease Control and Prevention (CDC) GWI definitions: (a) KS Symptoms (KS Sym+), (b) KS GWI (met symptom criteria and without exclusionary health conditions) [KS GWI: Sym+/Dx-], (c) CDC GWI and (d) CDC GWI Severe. The prevalence of each phenotype was 67.1% KS Sym+, 21.5% KS Sym+/Dx-, 81.1% CDC GWI, and 18.6% CDC GWI severe. These findings affirm the persistent presence of GWI among GW veterans providing a foundation for further exploration of biological and environmental underpinnings of this condition.

Genomics of Gulf War Illness in U.S. Veterans Who Served during the 1990-1991 Persian Gulf War: Methods and Rationale for Veterans Affairs Cooperative Study #2006.

BACKGROUND: Approximately 697,000 members of the U.S. Armed Forces were deployed to the Persian Gulf in support of the 1990-1991 Persian Gulf War (GW). Subsequently, many deployed and some non-deployed veterans developed a chronic multi-symptom illness, now named Gulf War Illness (GWI). This manuscript outlines the methods and rationale for studying the genomics of GWI within the Million Veteran Program (MVP), a VA-based national research program that has linked medical records, surveys, and genomic data, enabling genome-wide association studies (GWASs). METHODS: MVP participants who served in the military during the GW era were contacted by mail and invited to participate in the GWI study. A structured health questionnaire, based on a previously tested instrument, was also included in the mailing. Data on deployment locations and exposures, symptoms associated with GWI, clinical diagnoses, personal habits, and health care utilization were collected. Self-reported data will be augmented with chart reviews and structured international classification of disease codes, to classify participants by GWI case status. We will develop a phenotyping algorithm, based on two commonly used case definitions, to determine GWI status, and then conduct a nested case-control GWAS. Genetic variants associated with GWI will be investigated, and gene-gene and gene-environment interactions studied. The genetic overlap of GWI with, and causative mechanisms linking this illness to, other health conditions and the effects of genomic regulatory mechanisms on GWI risk will also be explored. CONCLUSIONS: The proposed initial GWAS described in this report will investigate the genomic underpinnings of GWI with a large sample size and state-of-the-art genomic analyses and phenotyping. The data generated will provide a rich and expansive foundation on which to build additional analyses.

Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions.

Major depressive disorder is the most common neuropsychiatric disorder, affecting 11% of veterans. Here we report results of a large meta-analysis of depression using data from the Million Veteran Program, 23andMe, UK Biobank and FinnGen, including individuals of European ancestry (n = 1,154,267; 340,591 cases) and African ancestry (n = 59,600; 25,843 cases). Transcriptome-wide association study analyses revealed significant associations with expression of NEGR1 in the hypothalamus and DRD2 in the nucleus accumbens, among others. We fine-mapped 178 genomic risk loci, and we identified likely pathogenicity in these variants and overlapping gene expression for 17 genes from our transcriptome-wide association study, including TRAF3. Finally, we were able to show substantial replications of our findings in a large independent cohort (n = 1,342,778) provided by 23andMe. This study sheds light on the genetic architecture of depression and provides new insight into the interrelatedness of complex psychiatric traits.

Racial and Ethnic Disparities in U.S. Veteran Health Characteristics.

Racial/ethnic health disparities persist among veterans despite comparable access and quality of care. We describe racial/ethnic differences in self-reported health characteristics among 437,413 men and women (mean age (SD) = 64.5 (12.6), 91% men, 79% White) within the Million Veteran Program. The Cochran-Mantel-Haenszel test and linear mixed models were used to compare age-standardized frequencies and means across race/ethnicity groups, stratified by gender. Black, Hispanic, and Other race men and women reported worse self-rated health, greater VA healthcare utilization, and more combat exposure than Whites. Compared to White men, Black and Other men reported more circulatory, musculoskeletal, mental health, and infectious disease conditions while Hispanic men reported fewer circulatory and more mental health, infectious disease, kidney, and neurological conditions. Compared to White women, Black women reported more circulatory and infectious disease conditions and Other women reported more infectious disease conditions. Smoking rates were higher among Black men, but lower for other minority groups compared to Whites. Minority groups were less likely to drink alcohol and had lower physical fitness than Whites. By identifying differences in burden of various health conditions and risk factors across different racial/ethnic groups, our findings can inform future studies and ultimately interventions addressing disparities.

A multicenter randomized placebo controlled trial of rifampin to reduce pedal amputations for osteomyelitis in veterans with diabetes (VA INTREPID).

BACKGROUND: The prevalence of diabetes mellitus continues to inexorably rise in the United States and throughout the world. Lower limb amputations are a devastating comorbid complication of diabetes mellitus. Osteomyelitis increases the risk of amputation fourfold and commonly presages death. Antimicrobial therapy for diabetic foot osteomyelitis (DFO) varies greatly, indicating that high quality data are needed to inform clinical decision making. Several small trials have indicated that the addition of rifampin to backbone antimicrobial regimens for osteomyelitis outside the setting of the diabetic foot results in 28 to 42% higher cure rates. METHODS/DESIGN: This is a prospective, randomized, double-blind investigation of the addition of 6 weeks of rifampin, 600 mg daily, vs. matched placebo (riboflavin) to standard-of-care, backbone antimicrobial therapy for DFO. The study population are patients enrolled in Veteran Health Administration (VHA), ages ≥18 and ≤ 89 years with diabetes mellitus and definite or probable osteomyelitis of the foot for whom an extended course of oral or intravenous antibiotics is planned. The primary endpoint is amputation-free survival. The primary hypothesis is that using rifampin as adjunctive therapy will lower the hazard rate compared with the group that does not use rifampin as adjunctive therapy. The primary hypothesis will be tested by means of a two-sided log-rank test with a 5% significance level. The test has 90% power to detect a hazard ratio of 0.67 or lower with a total of 880 study participants followed on average for 1.8 years. DISCUSSION: VA INTREPID will test if a rifampin-adjunctive antibiotic regimen increases amputation-free survival in patients seeking care in the VHA with DFO. A positive finding and its adoption by clinicians would reduce lower extremity amputations and their associated physical and emotional impact and reduce mortality for Veterans and for the general population with diabetic foot osteomyelitis. Given that rifampin-adjunctive regimens are currently employed for therapy for the majority of DFO cases in Europe, and only in a small minority of cases in the United States, the trial results will impact therapeutic decisions, even if the null hypothesis is not rejected. TRIAL REGISTRATION: Registered January 6, 2017 at ClinicalTrials.gov, NCT03012529.

Gender Differences in Demographic and Health Characteristics of the Million Veteran Program Cohort.

BACKGROUND: The Department of Veterans Affairs Million Veteran Program (MVP) is the largest ongoing cohort program of its kind, with 654,903 enrollees as of June 2018. The objectives of this study were to examine gender differences in the MVP cohort with respect to response and enrollment rates; demographic, health, and health care characteristics; and prevalence of self-reported health conditions. METHODS: The MVP Baseline Survey was completed by 415,694 veterans (8% women), providing self-report measures of demographic characteristics, health status, and medical history. RESULTS: Relative to men, women demonstrated a higher positive responder rate (23.0% vs. 16.0%), slightly higher enrollment rate (13.5% vs. 12.9%), and, among enrollees, a lower survey completion rate (59.7% vs. 63.8%). Women were younger, more racially diverse, had higher educational attainment, and were less likely to be married or cohabitating with a partner than men. Women were more likely to report good to excellent health status but poorer physical fitness, and less likely to report lifetime smoking and drinking than men. Compared with men, women veterans showed an increased prevalence of musculoskeletal conditions, thyroid problems, gastrointestinal conditions, migraine headaches, and mental health disorders, as well as a decreased prevalence of gout, cardiovascular diseases, high cholesterol, diabetes, and hearing problems. CONCLUSIONS: These results revealed some substantial gender differences in the research participation rates, demographic profile, health characteristics, and prevalence of health conditions for veterans in the MVP cohort. Findings highlight the need for tailoring recruitment efforts to ensure representation of the increasing women veteran population receiving care through the Veterans Health Administration.

Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans.

We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.

Spanish Abstracts by Asociación Chilena de Estrés Traumático (ACET) Validación de un algoritmo basado en registros médicos electrónicos para identificar el trastorno por estrés postraumático en veteranos de los EE. UU. VALIDACIÓN DE ALGORITOMO DE TEPT Desarrollamos un algoritmo para identificar a los veteranos de EE. UU. con historial de trastorno de estrés postraumático (TEPT), utilizando el sistema de registro médico electrónico (RME) del Departamento de Asuntos de Veteranos (AS). Este trabajo fue motivado por la necesidad de crear un fenotipo válido, basado en RME para identificar miles de casos y controles para un estudio de asociación del genoma del TEPT en los veteranos. Utilizamos la revisión manual de tablas (n = 500) como gold estándar. Tanto para el algoritmo como para la revisión de la tabla, fueron posibles tres clasificaciones: PTSD probable, PTSD posible y probablemente no PTSD. Usamos la regresión Lasso con validación cruzada para seleccionar los factores de pronóstico estadísticamente significativos del TEPT a partir de la RME y luego generar una puntuación de probabilidad pronosticada de ser un caso de TEPT para cada participante en la población del estudio (rango: 0-1.00). Comparando el rendimiento de nuestro enfoque probabilístico (algoritmo Lasso) con un enfoque basado en reglas (algoritmo de Clasificación Internacional de Enfermedades [CIE]), el algoritmo Lasso mostró un porcentaje de acuerdo global modestamente más alto con la revisión de tablas que el algoritmo CIE (80% vs. 75). %), mayor sensibilidad (0.95 frente a 0.84) y mayor precisión (AUC = 0.95 frente a 0.90). Aplicamos un punto de corte de probabilidad de 0.7 a los resultados de Lasso para determinar el estado final de control de caso de TEPT para la población de AV. El algoritmo final tuvo una sensibilidad de 0.99, una especificidad de 0.99, un valor predictivo positivo de 0.95 y un valor predictivo negativo de 1.00 para la clasificación de TEPT (agrupación de TEPT posible y probablemente no TEPT) según lo determinado por la revisión de la tabla. Este algoritmo puede ser útil para otros esfuerzos de investigación y mejora de la calidad dentro del AV.

Genomics of posttraumatic stress disorder in veterans: Methods and rationale for Veterans Affairs Cooperative Study #575B.

OBJECTIVES: Heritability in the risk for developing posttraumatic stress disorder (PTSD) has been established, but most genome-wide association studies (GWASs) of PTSD involve relatively small sample sizes and limited identification of associated genetic loci. This report describes the methodology of a Veterans Affairs (VA) Cooperative Studies Program GWAS of PTSD among combat-exposed U.S. veterans. METHODS: Probable cases (with PTSD) and probable controls (without PTSD) were identified from among veterans enrolled in the VA Million Veteran Program (MVP) with an algorithm developed using questionnaire responses and electronic health record information. This algorithm, based on a statistical model, relied on medical chart reviews as a reference standard and was refined using telephone interviews. Subsequently, to evaluate the impact of probabilistic phenotyping on statistical power, the threshold probability for case-control selection was varied in simulations. RESULTS: As of September 2018, >695,000 veterans have enrolled in MVP. For current analyses, genotyping data were available for >353,000 participants, including >83,000 combat-exposed veterans. A threshold probability of 0.7 for case and control designation yielded an interim >16,000 cases and >33,000 controls. CONCLUSIONS: A formal methodological approach was used to identify cases and controls for subsequent GWAS analyses to identify genetic risk loci for PTSD.

Design and conduct of a provider survey to determine a clinically persuasive effect size in planning VA Cooperative Study #590 (Li+).

BACKGROUND: The estimation of an effect size is an important step in designing an adequately powered, feasible clinical trial intended to change clinical practice. During the planning phase of VA Cooperative Study #590, "Double-Blind Placebo-Controlled Study of Lithium for Preventing Repeated Suicidal Self-Directed Violence in Patients with Depression or Bipolar Disorder (Li+)," it was not clear what effect size would be considered large enough to influence prescribing behavior among practicing clinicians. METHODS: We conducted an online survey of VA psychiatrists to assess their interest in the study question, their clinical experience with lithium, and their opinion about what suicide reduction rate would change their prescribing habits. The 9-item survey was hosted on SurveyMonkey© and VA psychiatrists were individually emailed an invitation to complete an anonymous online survey. Three email waves were sent over three weeks. RESULTS: Overall, 862 of 2713 VA psychiatrists (response rate = 31.8%) responded to the anonymous survey. 74% of the respondents would refer a patient to the proposed trial, 9% would not, and 17% were unsure. Presented with suicide reduction rates in 10% increments ranging from 10 to 100%, 61% of respondents indicated that they would use lithium if suicide attempts were reduced by at least 40%; 83% would use lithium if it reduced attempts by at least 50%. CONCLUSIONS: Even with the limitations of response bias and the reliability of responses on future prescribing behavior, a survey of potential users of a clinical trial's results offers a convenient, empirical method for determining and justifying clinically relevant effect sizes.

A dyadic analysis of the influence of trauma exposure and posttraumatic stress disorder severity on intimate partner aggression.

This study used structural equation modeling to evaluate a mediation model of the relationship between trauma exposure, posttraumatic stress disorder (PTSD) symptoms, and perpetration of intimate partner physical and psychological aggression in trauma-exposed veterans and their cohabitating spouses (n = 286 couples; 88% male veteran and female spouse, 80.8% White, non-Hispanic). Dyadic data analyses were used to simultaneously evaluate actor and partner effects using the actor-partner interdependence model (Kashy & Kenny, 2000). The primary hypothesis was that PTSD would mediate the association between trauma exposure and intimate partner physical and psychological aggression with these effects evident both within and across members of a couple (i.e., actor and partner effects). The best-fitting model included (a) equivalent actor and partner direct effects of trauma on veterans' acts of psychological aggression (ß = .17 to .20, p = .001), and (b) equivalent actor and partner indirect effects via PTSD on veterans' acts of physical aggression (ß = .08 to .10, p < .001). There were no direct or indirect effects predicting the spouses' aggression. Results suggest it is important to consider the trauma histories and possible presence of PTSD in both partners as this may be a point of intervention when treating distressed couples.

PTSD and conflict behavior between veterans and their intimate partners.

This study examined the influence of trauma history and PTSD symptoms on the behavior of veterans and their intimate partners (287 couples; N=574) observed during conflict discussions and coded using the Rapid Marital Interaction Coding System (Heyman, 2004). Dyadic structural equation modeling analyses showed that PTSD was associated with more frequent displays of hostility and psychological abuse and fewer expressions of acceptance and humor in both veterans and their partners. Findings provide new insight into the social and emotional deficits associated with PTSD and emphasize the importance of addressing the trauma histories and PTSD of both partners when treating veteran couples with relationship disturbance.

Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety.

Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad "lessons learned" for determining SEM sample size requirements are discussed.

Associations between Pittsburgh Sleep Quality Index factors and health outcomes in women with posttraumatic stress disorder.

OBJECTIVE: The Pittsburgh Sleep Quality Index (PSQI) is a widely used measure of subjective sleep disturbance in clinical populations, including individuals with posttraumatic stress disorder (PTSD). Although the severity of sleep disturbance is generally represented by a global symptom score, recent factor analytic studies suggest that the PSQI is better characterized by a two- or three-factor model than a one-factor model. This study examined the replicability of two- and three-factor models of the PSQI, as well as the relationship between PSQI factors and health outcomes, in a female sample with PTSD. METHODS: The PSQI was administered to 319 women with PTSD related to sexual or physical assault. Confirmatory factor analyses tested the relative fit of one-, two-, and three-factor solutions. Bivariate correlations were performed to examine the shared variance between PSQI sleep factors and measures of PTSD, depression, anger, and physical symptoms. RESULTS: Confirmatory factor analyses supported a three-factor model with Sleep Efficiency, Perceived Sleep Quality, and Daily Disturbances as separate indices of sleep quality. The severity of symptoms represented by the PSQI factors was positively associated with the severity of PTSD, depression, and physical symptoms. However, these health outcomes correlated as much or more with the global PSQI score as with PSQI factor scores. CONCLUSIONS: These results support the multidimensional structure of the PSQI. Despite this, the global PSQI score has as much or more explanatory power as individual PSQI factors in predicting health outcomes.