Community-engaged research/community-based participatory research/patient-engaged research (CEnR/CBPR/PEnR) are increasingly recognized as important approaches for addressing health equity. However, there is limited support for CEnR/CBPR/PEnR within Academic Health Centers (AHCs). It is important for AHCs to measure and monitor the context, process, and policies in support for CEnR/CBPR/PEnR. The Engage for Equity (E2) team developed the first Institutional Multi-Sector Survey (IMSS) instrument to assess and explore CEnR/CBPR/PEnR-related practices at three AHCs.

Working with “champion teams” consisting of academic leaders, researchers, and patient/community partners at each AHC, we developed the IMSS to assess the following domains: institutional mission, vision, and values; CEnR/CBPR/PEnR policies/practices; community processes/structures; function of formal community advisory boards; climate/culture for CEnR/CBPR; perceptions of institutional leadership for CEnR/CBPR/PEnR. The survey was piloted to a convenience sample of CEnR/CBPR/PEnR participants at each AHC site.

A sample aggregated across all sites consisting of community (n = 49) and academic (n = 50) participants perceived high levels of advocacy for CEnR/CBPR/PEnR among their AHC research teams. Participants indicated that institutional leadership supported CEnR/CBPR/PEnR in principle, but resources to build CEnR/CBPR/PEnR capacity at their respective institutions were lacking. Differences in responses from community and academic partners are summarized.

While limited by survey length and question adaptation, the findings contribute to identification of institutional barriers and facilitators to CEnR/CBPR/PEnR in AHCs. These findings are critically important to support and improve CEnR/CBPR/PEnR practice in academic institutions and to elevate community partner voices and needs for advancing community and patient partners’ research.

England's primary care service for psychological therapy (Improving Access to Psychological Therapies [IAPT]) treats anxiety and depression, with a target recovery rate of 50%. Identifying the characteristics of patients who achieve recovery may assist in optimizing future treatment. This naturalistic cohort study investigated pre-therapy characteristics as predictors of recovery and improvement after IAPT therapy.

In a cohort of patients attending an IAPT service in South London, we recruited 263 participants and conducted a baseline interview to gather extensive pre-therapy characteristics. Bayesian prediction models and variable selection were used to identify baseline variables prognostic of good clinical outcomes. Recovery (primary outcome) was defined using (IAPT) service-defined score thresholds for both depression (Patient Health Questionnaire [PHQ-9]) and anxiety (Generalized Anxiety Disorder [GAD-7]). Depression and anxiety outcomes were also evaluated as standalone (PHQ-9/GAD-7) scores after therapy. Prediction model performance metrics were estimated using cross-validation.

Predictor variables explained 26% (recovery), 37% (depression), and 31% (anxiety) of the variance in outcomes, respectively. Variables prognostic of recovery were lower pre-treatment depression severity and not meeting criteria for obsessive compulsive disorder. Post-therapy depression and anxiety severity scores were predicted by lower symptom severity and higher ratings of health-related quality of life (EuroQol questionnaire [EQ5D]) at baseline.

Almost a third of the variance in clinical outcomes was explained by pre-treatment symptom severity scores. These constructs benefit from being rapidly accessible in healthcare services. If replicated in external samples, the early identification of patients who are less likely to recover may facilitate earlier triage to alternative interventions.

Knowledge of sex differences in risk factors for posttraumatic stress disorder (PTSD) can contribute to the development of refined preventive interventions. Therefore, the aim of this study was to examine if women and men differ in their vulnerability to risk factors for PTSD.

As part of the longitudinal AURORA study, 2924 patients seeking emergency department (ED) treatment in the acute aftermath of trauma provided self-report assessments of pre- peri- and post-traumatic risk factors, as well as 3-month PTSD severity. We systematically examined sex-dependent effects of 16 risk factors that have previously been hypothesized to show different associations with PTSD severity in women and men.

Women reported higher PTSD severity at 3-months post-trauma. Z-score comparisons indicated that for five of the 16 examined risk factors the association with 3-month PTSD severity was stronger in men than in women. In multivariable models, interaction effects with sex were observed for pre-traumatic anxiety symptoms, and acute dissociative symptoms; both showed stronger associations with PTSD in men than in women. Subgroup analyses suggested trauma type-conditional effects.

Our findings indicate mechanisms to which men might be particularly vulnerable, demonstrating that known PTSD risk factors might behave differently in women and men. Analyses did not identify any risk factors to which women were more vulnerable than men, pointing toward further mechanisms to explain women's higher PTSD risk. Our study illustrates the need for a more systematic examination of sex differences in contributors to PTSD severity after trauma, which may inform refined preventive interventions.

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18–1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05–1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06–1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11–1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02–1.09), showing a genetic risk gradient across the conditions and the comorbidity.

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Psychiatric hospitalization is a major driver of cost in the treatment of schizophrenia. Here, we asked whether a technology-enhanced approach to relapse prevention could reduce days spent in a hospital after discharge.

The Improving Care and Reducing Cost (ICRC) study was a quasi-experimental clinical trial in outpatients with schizophrenia conducted between 26 February 2013 and 17 April 2015 at 10 different sites in the USA in an outpatient setting. Patients were between 18 and 60 years old with a diagnosis of schizophrenia, schizoaffective disorder, or psychotic disorder not otherwise specified. Patients received usual care or a technology-enhanced relapse prevention program during a 6-month period after discharge. The health technology program included in-person, individualized relapse prevention planning with treatments delivered via smartphones and computers, as well as a web-based prescriber decision support program. The main outcome measure was days spent in a psychiatric hospital during 6 months after discharge.

The study included 462 patients, of which 438 had complete baseline data and were thus used for propensity matching and analysis. Control participants (N = 89; 37 females) were enrolled first and received usual care for relapse prevention followed by 349 participants (128 females) who received technology-enhanced relapse prevention. During 6-month follow-up, 43% of control and 24% of intervention participants were hospitalized (χ2 = 11.76, p<0.001). Days of hospitalization were reduced by 5 days (mean days: b = −4.58, 95% CI −9.03 to −0.13, p = 0.044) in the intervention condition compared to control.

These results suggest that technology-enhanced relapse prevention is an effective and feasible way to reduce rehospitalization days among patients with schizophrenia.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

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.

Coordinated specialty care (CSC) is widely accepted as an evidence-based treatment for first episode psychosis (FEP). The NAVIGATE intervention from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) study is a CSC intervention which offers a suite of evidence-based treatments shown to improve engagement and clinical outcomes, especially in those with shorter duration of untreated psychosis (DUP). Coincident with the publication of this study, legislation was passed by the United States Congress in 2014–15 to fund CSC for FEP via a Substance Abuse and Mental Health Services Administration (SAMHSA) block grant set-aside for each state. In Michigan (MI) the management of this grant was delegated to Network180, the community mental health authority in Kent County, with the goal of making CSC more widely available to the 10 million people in MI. Limited research describes the outcomes of implementation of CSC into community practices with no published accounts evaluating the use of the NAVIGATE intervention in a naturalistic setting. We describe the outcomes of NAVIGATE implementation in the state of MI.

In 2014, 3 centers in MI were selected and trained to provide NAVIGATE CSC for FEP. In 2016 a 4th center was added, and 2 existing centers were expanded to provide additional access to NAVIGATE. Inclusion: age 18–31, served in 1 of 4 FEP centers in MI. Data collection began in 2015 for basic demographics, global illness (CGI q3 mo), hospital/ED use and work/school (SURF q3 mo) and was expanded in 2016 to include further demographics, diagnosis, DUP, vital signs; and in 2018 for clinical symptoms with the modified Colorado Symptom Inventory (mCSI q6 mo), reported via an online portal. This analysis used data until 12/31/19. Mixed effects models adjusted by age, sex and race were used to account for correlated data within patients.

N=283 had useable demographic information and were included in the analysis. Age at enrollment was 21.6 ± 3.0 yrs; 74.2% male; 53.4% Caucasian, 34.6% African American; 12.9 ± 1.7 yrs of education (N=195). 18 mo retention was 67% with no difference by sex or race. CGI scores decreased 20% from baseline (BL) to 18 mo (BL=3.5, N=134; 15–18 mo=2.8, N=60). Service utilization via the SURF was measured at BL (N=172) and 18 mo (N=72): psychiatric hospitalizations occurred in 37% at BL and 6% at 18 mo (p<0.01); ER visits occurred in 40% at BL and 13% at 18 mo (p<0.01). 44% were working or in school at BL and 68% at 18 mo (p<0.01). 21% were on antipsychotics (AP) at BL (N=178) and 85% at 18 mo (N=13) with 8% and 54% on long acting injectable-AP at BL and 18 mo, respectively. Limitations include missing data and lack of a control group.

The implementation of the NAVIGATE CSC program for FEP in MI resulted in meaningful clinical improvement for enrollees. Further support could make this evidence-based intervention available to more people with FEP.

Supported by funds from the SAMHSA Medicaid State Block Grant set-aside awarded to Network180 (Achtyes, Kempema). The funders had no role in the design of the study, the analysis or the decision to publish the results.

Little is known about emotional quality-of-life in paediatric heart disease in low- and middle-income countries where the prevalence of uncorrected lesions is high. Research on emotional quality-of-life and its predictors in these settings is key to planning interventions.

Ten-year retrospective cross-sectional study of children aged 6–17 years with uncorrected congenital or acquired heart disease in 12 low- and middle-income countries was conducted. Emotional functioning score of the PedsQL TM 4.0 generic core scale and data on patient-reported limitation in sports participation were collected via in-person interview and analysed using regression analyses.

Ninety-four children reported mean emotional functioning scores of 71.94 (SD 25.32) [95% CI 66.75–77.13] with lower scores independently associated with having a parent with a chronic illness or who had died (p = 0.005), having less than three siblings (p = 0.007), and reporting a subjective limitation in carrying an item equivalent to a 4 lb load (p = 0.021). Patient-reported limitation in sports participation at least “sometimes” was present in 69% and was independently associated with experiencing symptoms at least once a month (p < 0.001).

Some of the factors which were associated with better emotional quality-of-life were similar to those identified in previous studies in patients with corrected defects. Patient-reported limitation in sports participation is common. In addition to corrective surgery and exercise, numerous other interventions which are practicable during surgical missions might improve emotional quality-of-life.

In 2010, South Africa (SA) hosted the Fédération Internationale de Football Association (FIFA) World Cup (soccer). Emergency Medical Services (EMS) used the SA mass gathering medicine (MGM) resource model to predict resource allocation. This study analyzed data from the World Cup and compared them with the resource allocation predicted by the SA mass gathering model.

Prospectively, data were collected from patient contacts at 9 venues across the Western Cape province of South Africa. Required resources were based on the number of patients seeking basic life support (BLS), intermediate life support (ILS), and advanced life support (ALS). Overall patient presentation rates (PPRs) and transport to hospital rates (TTHRs) were also calculated.

BLS services were required for 78.4% (n = 1279) of patients and were consistently overestimated using the SA mass gathering model. ILS services were required for 14.0% (n = 228), and ALS services were required for 3.1% (n = 51) of patients. Both ILS and ALS services, and TTHR were underestimated at smaller venues.

The MGM predictive model overestimated BLS requirements and inconsistently predicted ILS and ALS requirements. MGM resource models, which are heavily based on predicted attendance levels, have inherent limitations, which may be improved by using research-based outcomes.