Higher inflammation has been linked to poor physical and mental health outcomes, and mortality, but few studies have rigorously examined whether changes in perceived stress and depressive symptoms are associated with increased inflammation within family caregivers and non-caregivers in a longitudinal design.

Longitudinal Study.

REasons for Geographic And Racial Differences in Stroke cohort study.

Participants included 239 individuals who were not caregivers at baseline but transitioned to providing substantial and sustained caregiving over time. They were initially matched to 241 non-caregiver comparisons on age, sex, race, education, marital status, self-rated health, and history of cardiovascular disease. Blood was drawn at baseline and approximately 9.3 years at follow-up for both groups.

Perceived Stress Scale, Center for Epidemiological Studies-Depression, inflammatory biomarkers, including high-sensitivity C-reactive protein, D dimer, tumor necrosis factor alpha receptor 1, interleukin (IL)-2, IL-6, and IL-10 taken at baseline and follow-up.

Although at follow-up, caregivers showed significantly greater worsening in perceived stress and depressive symptoms compared to non-caregivers, there were few significant associations between depressive symptoms or perceived stress on inflammation for either group. Inflammation, however, was associated with multiple demographic and health variables, including age, race, obesity, and use of medications for hypertension and diabetes for caregivers and non-caregivers.

These findings illustrate the complexity of studying the associations between stress, depressive symptoms, and inflammation in older adults, where these associations may depend on demographic, disease, and medication effects. Future studies should examine whether resilience factors may prevent increased inflammation in older caregivers.

To determine the incidence of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among healthcare personnel (HCP) and to assess occupational risks for SARS-CoV-2 infection.

Prospective cohort of healthcare personnel (HCP) followed for 6 months from May through December 2020.

Large academic healthcare system including 4 hospitals and affiliated clinics in Atlanta, Georgia.

HCP, including those with and without direct patient-care activities, working during the coronavirus disease 2019 (COVID-19) pandemic.

Incident SARS-CoV-2 infections were determined through serologic testing for SARS-CoV-2 IgG at enrollment, at 3 months, and at 6 months. HCP completed monthly surveys regarding occupational activities. Multivariable logistic regression was used to identify occupational factors that increased the risk of SARS-CoV-2 infection.

Of the 304 evaluable HCP that were seronegative at enrollment, 26 (9%) seroconverted for SARS-CoV-2 IgG by 6 months. Overall, 219 participants (73%) self-identified as White race, 119 (40%) were nurses, and 121 (40%) worked on inpatient medical-surgical floors. In a multivariable analysis, HCP who identified as Black race were more likely to seroconvert than HCP who identified as White (odds ratio, 4.5; 95% confidence interval, 1.3–14.2). Increased risk for SARS-CoV-2 infection was not identified for any occupational activity, including spending >50% of a typical shift at a patient’s bedside, working in a COVID-19 unit, or performing or being present for aerosol-generating procedures (AGPs).

In our study cohort of HCP working in an academic healthcare system, <10% had evidence of SARS-CoV-2 infection over 6 months. No specific occupational activities were identified as increasing risk for SARS-CoV-2 infection.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.

Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.

All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.

The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

This study investigated subjective memory complaints in older adults and the roles of setting, response bias, and personality.

Cognitively normal older adults from two settings completed questionnaires measuring memory complaints, response bias, and personality.

(A) Neuroimaging study with community-based recruitment and (B) academic memory clinic.

Cognitively normal older adults who (A) volunteer for research (N = 92) or (B) self-referred to a memory clinic (N = 20).

Neuropsychological evaluation and adjudication of normal cognitive status were done by the neuroimaging study or memory clinic. This study administered self-reports of subjective memory complaints, response bias, five-factor personality, and depressive symptoms. Primary group differences were examined with secondary sensitivity analyses to control for sex, age, and education differences.

There was no significant difference in over-reporting response bias between study settings. Under-reporting response bias was higher in volunteers. Cognitive complaints were associated with response bias for two cognitive complaint measures. Neuroticism was positively associated with over-reporting in evaluation-seekers and negatively associated with under-reporting in volunteers. The relationship was reversed for Extraversion. Under-reporting bias was positively correlated with Agreeableness and Conscientiousness in volunteers.

Evaluation-seekers do not show bias toward over-reporting symptoms compared to volunteers. Under-reporting response bias may be important to consider when screening for memory impairment in non-help-seeking settings. The Memory Functioning Questionnaire was less sensitive to reporting biases. Over-reporting may be a facet of higher Neuroticism. Findings help elucidate psychological influences on self-perceived cognitive decline and help seeking in aging and may inform different strategies for assessment by setting.

To characterize the prevalence of and seasonal and regional variation in inpatient antibiotic use among hospitalized US children in 2017–2018.

We conducted a cross-sectional examination of hospitalized children. The assessments were conducted on a single day in spring (May 3, 2017), summer (August 2, 2017), fall (October 25, 2017), and winter (January 31, 2018). The main outcome of interest was receipt of an antibiotic on the study day.

The study included 51 freestanding US children’s hospitals that participate in the Pediatric Health Information System (PHIS).

This study included all patients <18 years old who were admitted to a participating PHIS hospital, excluding patients who were admitted solely for research purposes.

Of 52,769 total hospitalized children, 19,174 (36.3%) received antibiotics on the study day and 6,575 of these (12.5%) received broad-spectrum antibiotics. The overall prevalence of antibiotic use varied across hospitals from 22.3% to 51.9%. Antibiotic use prevalence was 29.2% among medical patients and 47.7% among surgical patients. Although there was no significant seasonal variation in antibiotic use prevalence, regional prevalence varied, ranging from 32.7% in the Midwest to 40.2% in the West (P < .001). Among units, pediatric intensive care unit patients had the highest prevalence of both overall and broad-spectrum antibiotic use at 58.3% and 26.6%, respectively (P < .001).

On any given day in a national network of children’s hospitals, more than one-third of hospitalized children received an antibiotic, and 1 in 8 received a broad-spectrum antibiotic. Variation across hospitals, setting and regions identifies potential opportunities for enhanced antibiotic stewardship activities.

UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.

Describe the development, implementation and results of this questionnaire.

An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.

A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.

To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.

Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.

Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.