Incarceration is a significant social determinant of health, contributing to high morbidity, mortality, and racialized health inequities. However, incarceration status is largely invisible to health services research due to inadequate clinical electronic health record (EHR) capture. This study aims to develop, train, and validate natural language processing (NLP) techniques to more effectively identify incarceration status in the EHR.

The study population consisted of adult patients (≥ 18 y.o.) who presented to the emergency department between June 2013 and August 2021. The EHR database was filtered for notes for specific incarceration-related terms, and then a random selection of 1,000 notes was annotated for incarceration and further stratified into specific statuses of prior history, recent, and current incarceration. For NLP model development, 80% of the notes were used to train the Longformer-based and RoBERTa algorithms. The remaining 20% of the notes underwent analysis with GPT-4.

There were 849 unique patients across 989 visits in the 1000 annotated notes. Manual annotation revealed that 559 of 1000 notes (55.9%) contained evidence of incarceration history. ICD-10 code (sensitivity: 4.8%, specificity: 99.1%, F1-score: 0.09) demonstrated inferior performance to RoBERTa NLP (sensitivity: 78.6%, specificity: 73.3%, F1-score: 0.79), Longformer NLP (sensitivity: 94.6%, specificity: 87.5%, F1-score: 0.93), and GPT-4 (sensitivity: 100%, specificity: 61.1%, F1-score: 0.86).

Our advanced NLP models demonstrate a high degree of accuracy in identifying incarceration status from clinical notes. Further research is needed to explore their scaled implementation in population health initiatives and assess their potential to mitigate health disparities through tailored system interventions.

n-3 fatty acid consumption during pregnancy is recommended for optimal pregnancy outcomes and offspring health. We examined characteristics associated with self-reported fish or n-3 supplement intake.

Pooled pregnancy cohort studies.

Cohorts participating in the Environmental influences on Child Health Outcomes (ECHO) consortium with births from 1999 to 2020.

A total of 10 800 pregnant women in twenty-three cohorts with food frequency data on fish consumption; 12 646 from thirty-five cohorts with information on supplement use.

Overall, 24·6 % reported consuming fish never or less than once per month, 40·1 % less than once a week, 22·1 % 1–2 times per week and 13·2 % more than twice per week. The relative risk (RR) of ever (v. never) consuming fish was higher in participants who were older (1·14, 95 % CI 1·10, 1·18 for 35–40 v. <29 years), were other than non-Hispanic White (1·13, 95 % CI 1·08, 1·18 for non-Hispanic Black; 1·05, 95 % CI 1·01, 1·10 for non-Hispanic Asian; 1·06, 95 % CI 1·02, 1·10 for Hispanic) or used tobacco (1·04, 95 % CI 1·01, 1·08). The RR was lower in those with overweight v. healthy weight (0·97, 95 % CI 0·95, 1·0). Only 16·2 % reported n-3 supplement use, which was more common among individuals with a higher age and education, a lower BMI, and fish consumption (RR 1·5, 95 % CI 1·23, 1·82 for twice-weekly v. never).

One-quarter of participants in this large nationwide dataset rarely or never consumed fish during pregnancy, and n-3 supplement use was uncommon, even among those who did not consume fish.

The coronavirus disease 2019 (COVID-19) pandemic deleteriously impacted physical and mental health. In the summer of 2020, return-to-learn plans were enacted, including virtual, hybrid, and in-person plans, impacting educators and students. We examined (1) how return-to-learn plan was related to depressive and social anxiety symptoms among educators and (2) how psychological flexibility related to symptoms.

Educators (N = 853) completed a survey via Qualtrics that assessed internalizing symptoms, psychological flexibility, and occupational characteristics. Two one-way analyses of variance (ANOVAs) examined between-group differences in return-to-learn plans across depression and social anxiety. Two hierarchical linear regressions examined the relation between psychological flexibility components and depressive and social anxiety symptoms.

Median T-scores were well above the national normative means for General Depression (median T-score: 81) and Social Anxiety (median T-score: 67). There were no significant differences between reopening plans in general depression nor social anxiety T-scores. Psychological flexibility accounted for 33% of the variance in depressive symptoms and 24% of the variance in social anxiety symptoms.

Results indicated high levels of psychiatric symptoms among educators during COVID-19, and psychological flexibility was associated with lower symptoms. Addressing educator mental health is of utmost importance in future research.

There is increasing recognition of cognitive and pathological heterogeneity in early-stage Alzheimer’s disease and other dementias. Data-driven approaches have demonstrated cognitive heterogeneity in those with mild cognitive impairment (MCI), but few studies have examined this heterogeneity and its association with progression to MCI/dementia in cognitively unimpaired (CU) older adults. We identified cluster-derived subgroups of CU participants based on comprehensive neuropsychological data and compared baseline characteristics and rates of progression to MCI/dementia or a Dementia Rating Scale (DRS) of <129 across subgroups.

A hierarchical cluster analysis was conducted using 11 baseline neuropsychological test scores from 365 CU participants in the UCSD Shiley-Marcos Alzheimer’s Disease Research Center (age M=71.93 years, SD=7.51; 55.9% women; 15.6% Hispanic/Latino/a/x/e). A discriminate function analysis was then conducted to test whether the individual neuropsychological scores predicted cluster-group membership. Cox regressions examined the risk of progression to consensus diagnosis of MCI or dementia, or to DRS score <129, by cluster group.

Cluster analysis identified 5 groups: All-Average (n=139), Low-Visuospatial (n=46), Low-Executive (n=51), Low-Memory/Language (n=83), and Low-All Domains (n=46). The discriminant function analysis using the neuropsychological measures to predict group membership into these 5 clusters correctly classified 85.2% of the participants. Subgroups had unique demographic and clinical characteristics. Relative to the All-Average group, the Low-Visuospatial (hazard ratio [HR] 2.39, 95% CI [1.03, 5.56], p=.044), Low-Memory/Language (HR 4.37, 95% CI [2.24, 8.51], p<.001), and Low-All Domains (HR 7.21, 95% CI [3.59, 14.48], p<.001) groups had greater risk of progression to MCI/dementia. The Low-Executive group was also twice as likely to progress to MCI/dementia compared to the AllAverage group, but did not statistically differ (HR 2.03, 95% CI [0.88,4.70], p=.096). A similar pattern of results was found for progression to DRS score <129, with the Low-Executive (HR 2.82, 95% CI [1.26, 6.29], p=.012), Low-Memory/Language (HR 3.70, 95% CI [1.80, 7.56], p<.001) and Low-All Domains (HR 5.79, 95% CI [2.74, 12.27], p<.001) groups at greater risk of progression to a DRS score <129 than the All-Average group. The Low-Visuospatial group was also twice as likely to progress to DRS <129 compared to the All-Average group, but did not statistically differ (HR 2.02, 95% CI [0.80, 5.06], p=.135).

Our results add to a growing literature documenting heterogeneity in the earliest cognitive and pathological presentations associated with Alzheimer’s disease and related disorders. Participants with subtle memory/language, executive, and visuospatial weaknesses all declined at faster rates than the All-Average group, suggesting that there are multiple pathways and/or unique subtle cognitive decline profiles that ultimately lead to a diagnosis of MCI/dementia. These results have important implications for early identification of individuals at risk for MCI/dementia. Given that the same classification approach may not be optimal for everyone, determining profiles of subtle cognitive difficulties in CU individuals and implementing neuropsychological test batteries that assess multiple cognitive domains may be a key step towards an individualized approach to early detection and fewer missed opportunities for early intervention.

As part of the Research Domain Criteria (RDoC) initiative, the NIMH seeks to improve experimental measures of cognitive and positive valence systems for use in intervention research. However, many RDoC tasks have not been psychometrically evaluated as a battery of measures. Our aim was to examine the factor structure of 7 such tasks chosen for their relevance to schizophrenia and other forms of serious mental illness. These include the n-back, Sternberg, and self-ordered pointing tasks (measures of the RDoC cognitive systems working memory construct); flanker and continuous performance tasks (measures of the RDoC cognitive systems cognitive control construct); and probabilistic learning and effort expenditure for reward tasks (measures of reward learning and reward valuation constructs).

The sample comprised 286 cognitively healthy participants who completed novel versions of all 7 tasks via an online recruitment platform, Prolific, in the summer of 2022. The mean age of participants was 38.6 years (SD = 14.5, range 18-74), 52% identified as female, and stratified recruitment ensured an ethnoracially diverse sample. Excluding time for instructions and practice, each task lasted approximately 6 minutes. Task order was randomized. We estimated optimal scores from each task including signal detection d-prime measures for the n-back, Sternberg, and continuous performance task, mean accuracy for the flanker task, win-stay to win-shift ratio for the probabilistic learning task, and trials completed for the effort expenditure for reward task. We used parallel analysis and a scree plot to determine the number of latent factors measured by the 7 task scores. Exploratory factor analysis with oblimin (oblique) rotation was used to examine the factor loading matrix.

The scree plot and parallel analyses of the 7 task scores suggested three primary factors. The flanker and continuous performance task both strongly loaded onto the first factor, suggesting that these measures are strong indicators of cognitive control. The n-back, Sternberg, and self-ordered pointing tasks strongly loaded onto the second factor, suggesting that these measures are strong indicators of working memory. The probabilistic learning task solely loaded onto the third factor, suggesting that it is an independent indicator of reinforcement learning. Finally, the effort expenditure for reward task modestly loaded onto the second but not the first and third factors, suggesting that effort is most strongly related to working memory.

Our aim was to examine the factor structure of 7 RDoC tasks. Results support the RDoC suggestion of independent cognitive control, working memory, and reinforcement learning. However, effort is a factorially complex construct that is not uniquely or even most strongly related to positive valance. Thus, there is reason to believe that the use of at least 6 of these tasks are appropriate measures of constructs such as working memory, reinforcement learning and cognitive control.

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.

Agricultural workers are immersed in environments associated with increased risk for adverse psychiatric and neurological outcomes. Agricultural work-related risks to brain health include exposure to pesticides, heavy metals, and organic dust. Despite this, there is a gap in our understanding of the underlying brain systems impacted by these risks. This study explores clinical and cognitive domains, and functional brain activity in agricultural workers. We hypothesized that a history of agricultural work-related risks would be associated with poorer clinical and cognitive outcomes as well as changes in functional brain activity within cortico-striatal regions.

The sample comprised 17 agricultural workers and a comparison group of 45 non-agricultural workers recruited in the Northern Colorado area. All participants identified as White and non-Hispanic. The mean age of participants was 51.7 years (SD = 21.4, range 18-77), 60% identified as female, and 37% identified as male. Participants completed the National Institute of Health Toolbox (NIH Toolbox) and Montreal Cognitive Assessment (MoCA) on their first visit. During the second visit, they completed NIH Patient-Reported Outcomes Measurement Information System (PROMIS) measures and underwent functional magnetic resonance imaging (fMRI; N = 15 agriculture and N = 35 non-agriculture) while completing a working memory task (Sternberg). Blood oxygen-level dependent (BOLD) response was compared between participants. Given the small sample size, the whole brain voxel-wise group comparison threshold was set at alpha = .05, but not otherwise corrected for multiple comparisons. Cohen’s d effect sizes were estimated for all voxels.

Analyses of cognitive scores showed significant deficits in episodic memory for the agricultural work group. Additionally, the agricultural work group scored higher on measures of self-reported anger, cognitive concerns, and social participation. Analyses of fMRI data showed increased BOLD activity around the orbitofrontal cortex (medium to large effects) and bilaterally in the entorhinal cortex (large effects) for the agricultural work group. The agricultural work group also showed decreased BOLD activity in the cerebellum and basal ganglia (medium to large effects).

To our knowledge, this study provides the first-ever evidence showing differences in brain activity associated with a history of working in agriculture. These findings of poorer memory, concerns about cognitive functioning, and increased anger suggest clinical relevance. Social participation associated with agricultural work should be explored as a potential protective factor for cognition and brain health. Brain imaging data analyses showed increased activation in areas associated with motor functioning, cognitive control, and emotion. These findings are limited by small sample size, lack of diversity in our sample, and coarsely defined risk. Despite these limitations, the results are consistent with an overall concern that risks associated with agricultural work can lead to cognitive and psychiatric harm via changes in brain health. Replications and future studies with larger sample sizes, more diverse participants, and more accurately defined risks (e.g., pesticide exposure) are needed.

To effectively diagnose and treat cognitive post-COVID-19 symptoms, it is important to understand objective cognitive difficulties across the range of acute COVID-19 severity. The aim of this meta-analysis is to describe objective neuropsychological test performance in individuals with non-severe (mild/moderate) COVID-19 cases in the post-acute stage of infection (>28 days after initial infection).

This meta-analysis was pre-registered with Prospero (CRD42021293124) and utilized the PRISMA reporting guidelines, with screening conducted by at least two independent reviewers for all aspects of the screening and data extraction process. Inclusion criteria were established before the article search and were as follows: (1) Studies using adult participants with a probable or formal and documented diagnosis of COVID-19 in the post-acute stage of infection; (2) Studies comparing cognitive functioning using objective neuropsychological tests in one or more COVID-19 groups and a comparison group, or one group designs using tests with normative data; (3) Asymptomatic, mild, or moderate cases of COVID-19. Twenty-seven articles (n=18,202) with three types of study designs and three articles with additional longitudinal data met our full criteria.

Individuals with non-severe initial COVID-19 infection demonstrated worse cognitive performance compared to healthy comparison participants (d=-0.412 [95% CI, -0.718, -0.176)], p=0.001). We used metaregression to examine the relationship between both average age of the sample and time since initial COVID-19 infection (as covariates in two independent models) and effect size in studies with comparison groups. There was no significant effect for age (b=-0.027 [95% CI (0.091, 0.038)], p=0.42). There was a significant effect for time since diagnosis, with a small improvement in cognitive performance for every day following initial acute COVID-19 infection (b=0.011 [95% CI (0.0039, 0.0174)], p=0.002). However, those with mild (non-hospitalized) initial COVID-19 infections performed better than did those who were hospitalized for initial COVID-19 infections (d=0.253 [95% CI (0.372, 0.134)], p<0.001). For studies that used normative data comparisons, there was a small, non-significant effect compared to normative data (d=-0.165 [95% CI (-0.333, 0.003)], p=0.055).

Individuals who have recovered from non-severe cases of COVID-19 may be at risk for cognitive decline or impairment and may benefit from cognitive health interventions.