Adverse childhood experiences (ACEs) are associated with physical and mental health difficulties in adulthood. This study examines the associations of ACEs with functional impairment and life stress among military personnel, a population disproportionately affected by ACEs. We also evaluate the extent to which the associations of ACEs with functional outcomes are mediated through internalizing and externalizing disorders.

The sample included 4,666 STARRS Longitudinal Study (STARRS-LS) participants who provided information about ACEs upon enlistment in the US Army (2011–2012). Mental disorders were assessed in wave 1 (LS1; 2016–2018), and functional impairment and life stress were evaluated in wave 2 (LS2; 2018–2019) of STARRS-LS. Mediation analyses estimated the indirect associations of ACEs with physical health-related impairment, emotional health-related impairment, financial stress, and overall life stress at LS2 through internalizing and externalizing disorders at LS1.

ACEs had significant indirect effects via mental disorders on all functional impairment and life stress outcomes, with internalizing disorders displaying stronger mediating effects than externalizing disorders (explaining 31–92% vs 5–15% of the total effects of ACEs, respectively). Additionally, ACEs exhibited significant direct effects on emotional health-related impairment, financial stress, and overall life stress, implying ACEs are also associated with these longer-term outcomes via alternative pathways.

This study indicates ACEs are linked to functional impairment and life stress among military personnel in part because of associated risks of mental disorders, particularly internalizing disorders. Consideration of ACEs should be incorporated into interventions to promote psychosocial functioning and resilience among military personnel.

Migraine and post-traumatic stress disorder (PTSD) are both twice as common in women as men. Cross-sectional studies have shown associations between migraine and several psychiatric conditions, including PTSD. PTSD is disproportionally common among patients in headache clinics, and individuals with migraine and PTSD report greater disability from migraines and more frequent medication use. To further clarify the nature of the relationship between PTSD and migraine, we conducted bidirectional analyses of the association between (1) migraine and incident PTSD and (2) PTSD and incident migraine.

We used longitudinal data from 1989–2020 among the 33,327 Nurses’ Health Study II respondents to the 2018 stress questionnaire. We used log-binomial models to estimate the relative risk of developing PTSD among women with migraine and the relative risk of developing migraine among individuals with PTSD, trauma-exposed individuals without PTSD, and individuals unexposed to trauma, adjusting for race, education, marital status, high blood pressure, high cholesterol, alcohol intake, smoking, and body mass index.

Overall, 48% of respondents reported ever experiencing migraine, 82% reported experiencing trauma and 9% met the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for PTSD. Of those reporting migraine and trauma, 67% reported trauma before migraine onset, 2% reported trauma and migraine onset in the same year and 31% reported trauma after migraine onset. We found that migraine was associated with incident PTSD (adjusted relative risk [RR]: 1.26, 95% confidence interval [CI]: 1.14–1.39). PTSD, but not trauma without PTSD, was associated with incident migraine (adjusted RR: 1.20, 95% CI: 1.14–1.27). Findings were consistently stronger in both directions among those experiencing migraine with aura.

Our study provides further evidence that migraine and PTSD are strongly comorbid and found associations of similar magnitude between migraine and incident PTSD and PTSD and incident migraine.

Alterations in cerebral blood flow (CBF) are associated with risk of cognitive decline and Alzheimer’s disease (AD). Although apolipoprotein E (APOE) ε4 and greater vascular risk burden have both been linked to reduced CBF in older adults, less is known about how APOE ε4 status and vascular risk may interact to influence CBF. We aimed to determine whether the effect of vascular risk on CBF varies by gene dose of APOE ε4 alleles (i.e., number of e4 alleles) in older adults without dementia.

144 older adults without dementia from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) underwent arterial spin labeling (ASL) and T1-weighted MRI, APOE genotyping, fluorodeoxyglucose positron emission tomography (FDG-PET), lumbar puncture, and blood pressure assessment. Vascular risk was assessed using pulse pressure (systolic blood pressure -diastolic blood pressure), which is thought to be a proxy for arterial stiffening. Participants were classified by number of APOE ε4 alleles (n0 alleles = 87, m allele = 46, n2 alleles = 11). CBF in six FreeSurfer-derived a priori regions of interest (ROIs) vulnerable to AD were examined: entorhinal cortex, hippocampus, inferior temporal cortex, inferior parietal cortex, rostral middle frontal gyrus, and medial orbitofrontal cortex. Linear regression models tested the interaction between categorical APOE ε4 dose (0, 1, or 2 alleles) and continuous pulse pressure on CBF in each ROI, adjusting for age, sex, cognitive diagnosis (cognitively unimpaired vs. mild cognitive impairment), antihypertensive medication use, cerebral metabolism (FDG-PET composite), reference CBF region (precentral gyrus), and AD biomarker positivity defined using the ADNI-optimized phosphorylated tau/ß-amyloid ratio cut-off of > 0.0251 pg/ml.

A significant pulse pressure X APOE ε4 dose interaction was found on CBF in the entorhinal cortex, hippocampus, and inferior parietal cortex (ps < .005). Among participants with two e4 alleles, higher pulse pressure was significantly associated with lower CBF (ps < .001). However, among participants with zero or one ε4 allele, there was no significant association between pulse pressure and CBF (ps > .234). No significant pulse pressure X APOE ε4 dose interaction was found in the inferior temporal cortex, rostral middle frontal gyrus, or medial orbitofrontal cortex (ps > .109). Results remained unchanged when additionally controlling for general vascular risk assessed via the modified Hachinski Ischemic Scale.

These findings demonstrate that the cross-sectional association between pulse pressure and region-specific CBF differs by APOE ε4 dose. In particular, a detrimental effect of elevated pulse pressure on CBF in AD-vulnerable regions was found only among participants with the e4/e4 genotype. Our findings suggest that pulse pressure may play a mechanistic role in neurovascular unit dysregulation for those genetically at greater risk for AD. Given that pulse pressure is just one of many potentially modifiable vascular risk factors for AD, future studies should seek to examine how these other factors (e.g., diabetes, high cholesterol) may interact with APOE genotype to affect cerebrovascular dysfunction.

Higher educational attainment is associated with reduced risk for Alzheimer's disease (AD) dementia, and its protective effect may act through alterations in cerebral blood flow (CBF) that allow for better coping with accumulating neuropathology. Additionally, there are sex differences in both the risk of developing AD as well as the potential protective effects of education. We therefore sought to investigate whether education moderates the association of hippocampal CBF and memory in cognitively unimpaired older adults, and to examine if these interactions were moderated by sex.

Cognitively unimpaired older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI; 51 men, 50 women) underwent neuropsychological evaluation and arterial spin labeling MRI, which was used to quantify bilateral hippocampal CBF. Sex was defined as sex at birth. Multiple linear regressions assessed (1) the independent associations among education, CBF, and memory performance separately in men and women and (2) the three-way interactions among CBF, sex, and education, followed by sex-stratified analyses. Three outcome measures were examined: Logical Memory Story A immediate and delayed recall, and Rey Auditory Verbal Learning Test (RAVLT) intrusions. All models adjusted for age and APOE epsilon-4 allele frequency, and all models with CBF additionally adjusted for cerebral metabolism (baseline FDG-PET composite) and pulse pressure.

CBF was not associated with education or memory in either women or men. There was a positive association between education and delayed memory in women (ß=0.14, t=2.64, p=0.008) as well as trending, positive associations between education and immediate memory in women (ß=0.09, t=1.79, p=0.074) and education and delayed memory in men (ß=0.09, t=1.94, p=0.054). Three-way interactions among sex, CBF, and education were significant on immediate recall (ß=2.55, t=2.53, p=0.013), delayed recall (ß=2.56, t=2.44, p=0.017), and RAVLT intrusions (ß=-2.28, t=-2.27, p=0.026). In women, there were interactions between education and hippocampal CBF on both immediate (ß=2.49, t=2.90, p=0.006) and delayed recall (ß=2.30, t=2.78, p=0.009), such that as education increased, the strength of the association between CBF and immediate memory increased. There was also an interaction between education and hippocampal CBF on RAVLT intrusions in women (ß=-2.42, t=-3.05, p=0.004), such that as education increased, the strength of the association between CBF and number of intrusions decreased; there was a main effect where in women with lower education, as CBF increased, the number of intrusions increased (ß=0.76, t=2.59, p=0.032); in women with higher education, there was no association between CBF and intrusions. In men, none of these two-way interactions were significant.

These results suggest that, in cognitively unimpaired older women, the relationship between hippocampal CBF and memory is moderated by education level, even when adjusting for several other factors. Specifically, higher education may serve as a protective factor in the hippocampal CBF-memory relationship, and this relationship was sex-dependent, occurring in women only. Further research is needed to examine these relationships longitudinally across the clinical continuum of AD. Additionally, this work needs to be conducted in more diverse samples to allow for analyses investigating the impact of education on the intersection of race/ethnicity and sex/gender.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

To assess viewer engagement of a food advertising campaign on the live streaming platform Twitch.tv, a social media platform that allows creators to live stream content and communicate with their audience in real time.

Observational analysis of chat comments across the Twitch platform containing the word ‘Wendy’s’ or ‘Wendys’ during a 5-day ad campaign compared with two 5-day non-campaign time periods. Comments were categorised as positive, negative or neutral in how their sentiment pertained to the brand Wendy’s.

Twitch chatrooms.

None.

There were significantly more chatroom messages related to the Wendy’s brand during the campaign period. When considering all messages, the proportion of messages was statistically different (x2 = 1417·41, P < 0·001) across time periods, with a higher proportion of neutral and positive messages and a lower proportion of negative messages during the campaign compared with the comparison periods. Additionally, the proportion of negative messages following the campaign was lower than before the campaign. When considering only positive and negative messages, the proportion of messages was statistically different (x2 = 366·38, P < 0·001) across each time period with a higher proportion of positive messages and a lower proportion of negative messages during the campaign when compared with the other time periods. Additionally, there was a higher proportion of positive messages and a lower portion of negative messages following the campaign when compared with before the campaign.

This study demonstrates the impact and sustained impact of a fast-food brand ad campaign on brand engagement on the live streaming platform Twitch.

To determine the proportion of hospitals that implemented 6 leading practices in their antimicrobial stewardship programs (ASPs). Design: Cross-sectional observational survey.

Acute-care hospitals.

ASP leaders.

Advance letters and electronic questionnaires were initiated February 2020. Primary outcomes were percentage of hospitals that (1) implemented facility-specific treatment guidelines (FSTG); (2) performed interactive prospective audit and feedback (PAF) either face-to-face or by telephone; (3) optimized diagnostic testing; (4) measured antibiotic utilization; (5) measured C. difficile infection (CDI); and (6) measured adherence to FSTGs.

Of 948 hospitals invited, 288 (30.4%) completed the questionnaire. Among them, 82 (28.5%) had <99 beds, 162 (56.3%) had 100–399 beds, and 44 (15.2%) had ≥400+ beds. Also, 230 (79.9%) were healthcare system members. Moreover, 161 hospitals (54.8%) reported implementing FSTGs; 214 (72.4%) performed interactive PAF; 105 (34.9%) implemented procedures to optimize diagnostic testing; 235 (79.8%) measured antibiotic utilization; 258 (88.2%) measured CDI; and 110 (37.1%) measured FSTG adherence. Small hospitals performed less interactive PAF (61.0%; P = .0018). Small and nonsystem hospitals were less likely to optimize diagnostic testing: 25.2% (P = .030) and 21.0% (P = .0077), respectively. Small hospitals were less likely to measure antibiotic utilization (67.8%; P = .0010) and CDI (80.3%; P = .0038). Nonsystem hospitals were less likely to implement FSTGs (34.3%; P < .001).

Significant variation exists in the adoption of ASP leading practices. A minority of hospitals have taken action to optimize diagnostic testing and measure adherence to FSTGs. Additional efforts are needed to expand adoption of leading practices across all acute-care hospitals with the greatest need in smaller hospitals.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.