Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

The study objective was to develop and validate a clinical decision support system (CDSS) to guide clinicians through the diagnostic evaluation of hospitalized individuals with suspected pulmonary tuberculosis (TB) in low-prevalence settings.

The “TBorNotTB” CDSS was developed using a modified Delphi method. The CDSS assigns points based on epidemiologic risk factors, TB history, symptoms, chest imaging, and sputum/bronchoscopy results. Below a set point threshold, airborne isolation precautions are automatically discontinued; otherwise, additional evaluation, including infection control review, is recommended. The model was validated through retrospective application of the CDSS to all individuals hospitalized in the Mass General Brigham system from July 2016 to December 2022 with culture-confirmed pulmonary TB (cases) and equal numbers of age and date of testing-matched controls with three negative respiratory mycobacterial cultures.

104 individuals with TB (cases) and 104 controls were identified. Prior residence in a highly endemic country, positive interferon release assay, weight loss, absence of symptom resolution with treatment for alternative diagnoses, and findings concerning for TB on chest imaging were significant predictors of TB (all P < 0.05). CDSS contents and scoring were refined based on the case–control analysis. The final CDSS demonstrated 100% sensitivity and 27% specificity for TB with an AUC of 0.87.

The TBorNotTB CDSS demonstrated modest specificity and high sensitivity to detect TB even when AFB smears were negative. This CDSS, embedded into the electronic medical record system, could help reduce risks of nosocomial TB transmission, patient-time in airborne isolation, and person-time spent reviewing individuals with suspected TB.

Individuals who are unable to meet their basic needs are more likely to respond reactively to their immediate social and financial hardships with behaviors that lead to “diseases of despair,” which include suicide, drug overdose, and alcohol-induced liver diseases. We sought to assess the feasibility of a community-to-clinic referral approach for diseases of despair-related behaviors.

Guided by the Model for Adaptation Design and Impact, we adapted existing clinical risk assessments into a six-item screener and integrated it into the PA 211 Southwest helpline’s workflow. The screener was created to identify helpline callers at risk for suicidal ideation/behavior, alcohol abuse, drug use, and those in need of seasonal flu vaccination. The screener was implemented from December 2020 to March 2021. We invited at-risk individuals who accepted a service referral to complete baseline and follow-up surveys to learn about their satisfaction with screening and use of referrals.

2,868 callers were invited to take the screener, with 37% (n = 1047) participation. Among screened callers, 19% (n = 196) were at risk of alcohol abuse, 11% (n = 118) for drug use, 9% (n = 98) for suicidal ideation/behavior, and 54% (n = 568) needed flu vaccination. Of those, 265 callers accepted at least one of the offered referrals. Forty-seven individuals took our surveys, with almost half of them (n = 22) reported engaging with a referral and 90% recommended the helpline for health referrals.

Our findings demonstrate the feasibility of using existing community infrastructure and social service systems to actively screen and link at-risk individuals to needed health referrals in their communities.

Area Deprivation Index (ADI) is a measurement of neighborhood disadvantage. Evidence suggests that living in a disadvantaged neighborhood has a negative impact on health outcomes independent of socioeconomic status, including increased risk for Alzheimer's disease (AD). However, less is known about the biological mechanisms that drive these associations. We examined how ADI influences structural imaging variables and cognitive performance in community-dwelling older adults. We hypothesized that greater neighborhood disadvantage would predict atrophy and worse cognitive trajectory over time.

Participants included the legacy cohort from the Vanderbilt Memory and Aging Project (n=295, 73±7 years of age, 16±3 years of education, 42% female, 85% non-Hispanic White) who lived in the state of Tennessee. T1-weighted and T2-weighted fluid-attenuated inversion recovery brain MRIs and a comprehensive neuropsychological assessment were acquired at baseline, 18-month, 3-year, 5-year and 7-year follow-up time (mean follow-up time=5.2 years). Annual change scores were calculated for all neuropsychological and structural MRI outcome variables. Baseline state ADI was calculated using the University of Wisconsin School of Medicine and Public Health Neighborhood Atlas (Kind & Buckingham, 2018) and was based on deciles where 1 represents the least deprived area and 10 represents the most. Mixed effects regression models related baseline ADI to longitudinal brain structure (volume, thickness, white matter hyperintensities) and neuropsychological trajectory (one test per model). Analyses adjusted for age, sex, race/ethnicity, education, Framingham Stroke Risk Profile score, (apolipoprotein) APOE-e4 status, cognitive status, and intracranial volume (for MRI outcomes). Models were repeated testing interactions with APOE-e4 status, sex, and cognitive status. A false discovery rate (FDR) correction for multiple comparisons was performed.

On average, the sample was from relatively less disadvantaged neighborhoods in Tennessee (ADI state decile=2.4±1.8). Greater neighborhood disadvantage at study entry predicted more thinning of an AD-signature composite over time (ß=-0.002, p=0.005, pFDR=0.06); however, all other models testing MRI and neuropsychological outcomes were null (p-values>0.05, pFDR-values>0.51). Baseline ADI interacted with sex on longitudinal cortical thinning captured on the AD-signature composite (ß=0.004, p=0.006, pFDR=0.08) as well as several longitudinal cognitive outcomes including an executive function composite score (ß=0.033, p<0.001, pFDR=0.01), naming (ß=0.10, p=0.01, pFDR=0.12), visuospatial functioning (ß=0.083, p=0.02, pFDR=0.09), and an episodic memory composite score (ß=0.021, p=0.02, pFDR=0.07). In stratified models by sex, greater ADI predicted greater cortical thinning over time and worse longitudinal neuropsychological performance among men only. All stratified models in women were null except for executive function composite score, which did not survive correction for multiple comparisons (ß=-0.013, p=0.03, pFDR=0.61). Interactions by APOE-e4 and cognitive status were null (p-values>0.06, pFDR-values>0.61).

Among community-dwelling older adults, greater neighborhood disadvantage predicted greater cortical thinning over the mean 5-year follow-up in anatomical regions susceptible to AD-related neurodegeneration. Neighborhood disadvantage also interacted with sex on cortical thickness and several cognitive domains, with stronger effects found among men versus women. By contrast, there were no interactions between neighborhood disadvantage and genetic risk for AD or cognitive status. This study provides valuable evidence for sociobiological mechanisms that may underlie health disparities in aging adults whereby neighborhood deprivation is linked with neurodegeneration over time.

To quantify perceptions of tap water among low-income mothers with young children residing in Michigan and examine associations between perceptions of tap water, mothers’ and young children’s beverage intake, and mothers’ infant feeding practices.

Cross-sectional study.

Online survey.

Medicaid-insured individuals who had given birth at a large Midwestern US hospital between fall 2016 and fall 2020 were invited by email to complete a survey in winter 2020 (N 3881); 15·6 % (N 606) completed eligibility screening, 550 (90·8 %) were eligible to participate, and 500 (90·9 %) provided valid survey data regarding perceptions of tap water, self and child beverage intake, and infant feeding practices.

Two-thirds (66·2 %) of mothers reported that their home tap water was safe to drink without a filter, while 21·6 % were unsure about the safety of their home tap water. Mothers’ perceptions of their home tap water were associated with their own tap and bottled water intake and their young children’s tap water and bottled water intake. Mothers with more negative perceptions of tap water in general, independent of their perceptions about their home tap water, consumed more bottled water and sugar-sweetened beverages, and their young children drank bottled water and fruit drinks more frequently. Few associations were observed between mothers’ perceptions of tap water and infant feeding practices.

Uncertainty about tap water safety and negative perceptions of tap water are common among low-income Michigan mothers. These beliefs may contribute to less healthful and more costly beverage intake among mothers and their young children.

Unmet social needs contribute to growing health disparities and rising health care costs. Strategies to collect and integrate information on social needs into patients’ electronic health records (EHRs) show promise for connecting patients with community resources. However, gaps remain in understanding the contextual factors that impact implementing these interventions in clinical settings.

We conducted qualitative interviews with patients and focus groups with providers (January−September 2020) in two primary care clinics to inform the implementation of a module that collects and integrates patient-reported social needs information into the EHR. Questions addressed constructs within the Theoretical Framework for Acceptability and the Consolidated Framework for Implementation Research. Data were coded deductively using team-based framework analysis, followed by inductive coding and matrix analyses.

Forty patients participated in interviews, with 20 recruited at the clinics and 20 from home. Two focus groups were conducted with a total of 12 providers. Factors salient to acceptability and feasibility included patients’ discomfort answering sensitive questions, concerns about privacy, difficulty reading/understanding module content, and technological literacy. Rapport with providers was a facilitator for patients to discuss social needs. Providers stressed that limited time with patients would be a barrier, and expressed concerns about the lack of available community resources.

Findings highlight the need for flexible approaches to assessing and discussing social needs with patients. Feasibility of the intervention is contingent upon support from the health system to facilitate social needs assessment and discussion. Further study of availability of community resources is needed to ensure intervention effectiveness.

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.

Amongst patients with CHD, the time of transition to adulthood is associated with lapses in care leading to significant morbidity. The purpose of this study was to identify differences in perceptions between parents and teens in regard to transition readiness.

Responses were collected from 175 teen–parent pairs via the validated CHD Transition Readiness survey and an information request checklist. The survey was distributed via an electronic tablet at a routine clinic visit.

Parents reported a perceived knowledge gap of 29.2% (the percentage of survey items in which a parent believes their teen does not know), compared to teens self-reporting an average of 25.9% of survey items in which they feel deficient (p = 0.01). Agreement was lowest for long-term medical needs, physical activities allowed, insurance, and education. In regard to self-management behaviours, agreement between parent and teen was slight to moderate (weighted κ statistic = 0.18 to 0.51). For self-efficacy, agreement ranged from slight to fair (weighted κ = 0.16 to 0.28). Teens were more likely to request information than their parents (79% versus 65% requesting at least one item) particularly in regard to pregnancy/contraception and insurance.

Parents and teens differ in several key perceptions regarding knowledge, behaviours, and feelings related to the management of heart disease. Specifically, parents perceive a higher knowledge deficit, teens perceive higher self-efficacy, and parents and teens agree that self-management is low.

Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors.

To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions.

Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title.

In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7–8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0–22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1–10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors.

Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.