Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression.

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening.

OBJECTIVE: To test whether different clinical decision support tools increase clinician orders and patient completions relative to standard practice and each other. STUDY DESIGN: A pragmatic, patient-randomized clinical trial in the electronic health record was conducted between October 2019 and April 2020 at Geisinger Health System in Pennsylvania, with 4 arms: care gap-a passive listing recommending screening; alert-a panel promoting and enabling lipid screen orders; both; and a standard practice-no guideline-based notification-control arm. Data were analyzed for 13 346 9- to 11-year-old patients seen within Geisinger primary care, cardiology, urgent care, or nutrition clinics, or who had an endocrinology visit. Principal outcomes were lipid screening orders by clinicians and completions by patients within 1 week of orders. RESULTS: Active (care gap and/or alert) vs control arm patients were significantly more likely (P < .05) to have lipid screening tests ordered and completed, with ORs ranging from 1.67 (95% CI 1.28-2.19) to 5.73 (95% CI 4.46-7.36) for orders and 1.54 (95% CI 1.04-2.27) to 2.90 (95% CI 2.02-4.15) for completions. Alerts, with or without care gaps listed, outperformed care gaps alone on orders, with odds ratios ranging from 2.92 (95% CI 2.32-3.66) to 3.43 (95% CI 2.73-4.29). CONCLUSIONS: Electronic alerts can increase lipid screening orders and completions, suggesting clinical decision support can improve guideline-concordant screening. The study also highlights electronic record-based patient randomization as a way to determine relative effectiveness of support tools. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04118348.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Association Between Psychiatric Polygenic Scores, Healthcare Utilization and Comorbidity Burden.

Purpose: To estimate the association of psychiatric polygenic scores with healthcare utilization and comorbidity burden. Methods: Observational cohort study (N = 118,882) of adolescent and adult biobank participants with linked electronic health records (EHRs) from three diverse study sites; (Massachusetts General Brigham, Vanderbilt University Medical Center, Geisinger). Polygenic scores (PGS) were derived from the largest available GWAS of major depressive depression, bipolar disorder, and schizophrenia at the time of analysis. Negative binomial regression models were used to estimate the association between each psychiatric PGS and healthcare utilization and comorbidity burden. Healthcare utilization was measured as frequency of emergency department (ED), inpatient (IP), and outpatient (OP) visits. Comorbidity burden was defined by the Elixhauser Comorbidity Index and the Charlson Comorbidity Index. Results: Participants had a median follow-up duration of 12 years in the EHR. Individuals in the top decile of polygenic score for major depressive disorder had significantly more ED visits (RR=1.22, 95% CI; 1.17, 1.29) compared to those the lowest decile. Increases were also observed with IP and comorbidity burden. Among those diagnosed with depression and in the highest decile of the PGS, there was an increase in all utilization types (ED: RR=1.56, 95% CI 1.41, 1.72; OP: RR=1.16, 95% CI 1.08, 1.24; IP: RR=1.23, 95% CI 1.12, 1.36) post-diagnosis. No clinically significant results were observed with bipolar and schizophrenia polygenic scores. Conclusions: Polygenic score for depression is modestly associated with increased healthcare resource utilization and comorbidity burden, in the absence of diagnosis. Following a diagnosis of depression, the PGS was associated with further increases in healthcare utilization. These findings suggest that depression genetic risk is associated with utilization and burden of chronic disease in real-world settings.

An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability.

Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.

Experiment aversion among clinicians and the public - an obstacle to evidence-based medicine and public health.

Background: Randomized controlled trials (RCTs) are essential for determining the safety and efficacy of healthcare interventions. However, both laypeople and clinicians often demonstrate experiment aversion: preferring to implement either of two interventions for everyone rather than comparing them to determine which is best. We studied whether clinician and layperson views of pragmatic RCTs for Covid-19 or other interventions became more positive early in the pandemic, which increased both the urgency and public discussion of RCTs. Methods: We conducted several survey studies with laypeople (total n=2,909) and two with clinicians (n=895; n=1,254) in 2020 and 2021. Participants read vignettes in which a hypothetical decision-maker who sought to improve health could choose to implement intervention A for all, implement intervention B for all, or experimentally compare A and B and implement the superior intervention. Participants rated and ranked the appropriateness of each decision. Results: Compared to our pre-pandemic results, we found no decrease in laypeople's aversion to non-Covid-19 experiments involving catheterization checklists and hypertension drugs. Nor were either laypeople or clinicians less averse to Covid-19 RCTs (concerning corticosteroid drugs, vaccines, intubation checklists, proning, school reopening, and mask protocols), on average. Across all vignettes and samples, levels of experiment aversion ranged from 28% to 57%, while levels of experiment appreciation (in which the RCT is rated higher than the participant's highest-rated intervention) ranged from only 6% to 35%. Conclusions: Advancing evidence-based medicine through pragmatic RCTs will require anticipating and addressing experiment aversion among both patients and healthcare professionals.

Development and Multi-Site External Validation of a Generalizable Risk Prediction Model for Bipolar Disorder.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

The Social Shapes Test as a Self-Administered, Online Measure of Social Intelligence: Two Studies with Typically Developing Adults and Adults with Autism Spectrum Disorder.

The Social Shapes Test (SST) is a measure of social intelligence which does not use human faces or rely on extensive verbal ability. The SST has shown promising validity among adults without autism spectrum disorder (ASD), but it is uncertain whether it is suitable for adults with ASD. We find measurement invariance between adults with (n = 229) or without ASD (n = 1,049) on the 23-item SST. We also find that adults without ASD score higher on the SST than adults with ASD (d = 0.21). We also provide two, 14-item versions which demonstrated good parallel test-retest reliability and are positively related to scores on the Frith-Happé task. The SST is suitable for remote, online research studies.

Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping.

Importance: Developmental language disorder (DLD) is a common (with up to 7% prevalence) yet underdiagnosed childhood disorder whose underlying biological profile and comorbidities are not fully understood, especially at the population level. Objective: To identify clinically relevant conditions that co-occur with DLD at the population level. Design, Setting, and Participants: This case-control study used an electronic health record (EHR)-based population-level approach to compare the prevalence of comorbid health phenotypes between DLD cases and matched controls. These cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm of the Vanderbilt University Medical Center EHR, and a phenome enrichment analysis was used to identify comorbidities. An independent sample was selected from the Geisinger Health System EHR to test the replication of the phenome enrichment using the same phenotyping and analysis pipeline. Data from the Vanderbilt EHR were accessed between March 2019 and October 2020, while data from the Geisinger EHR were accessed between January and March 2022. Main Outcomes and Measures: Common and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis. Results: Comorbidity analysis was conducted for 5273 DLD cases (mean [SD] age, 16.8 [7.2] years; 3748 males [71.1%]) and 26 353 matched controls (mean [SD] age, 14.6 [5.5] years; 18 729 males [71.1%]). Relevant phenotypes associated with DLD were found, including learning disorder, delayed milestones, disorders of the acoustic nerve, conduct disorders, attention-deficit/hyperactivity disorder, lack of coordination, and other motor deficits. Several other health phenotypes not previously associated with DLD were identified, such as dermatitis, conjunctivitis, and weight and nutrition, representing a new window into the clinical complexity of DLD. Conclusions and Relevance: This study found both rare and common comorbidities of DLD. Comorbidity profiles may be leveraged to identify risk of additional health challenges, beyond language impairment, among children with DLD.

Using game-like animations of geometric shapes to simulate social interactions: An evaluation of group score differences.

This study introduces a novel, game-like method for measuring social intelligence: the Social Shapes Test. Unlike other existing video or game-based tests, the Shapes Test uses animations of abstract shapes to represent social interactions. We explore demographic differences in Shapes Test scores compared to a written situational judgment test. Gender and race/ethnicity only had meaningful effects on written SJT scores while no effects were found for Shapes Test scores. This pattern of results remained after controlling for general mental ability and English language exposure. We also found metric invariance between demographic groups for both tests. Our results demonstrate the potential for using animated shape tasks as an alternative to written SJTs when designing future game-based assessments.

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications.

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model's correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783−0.789], compared with 0.694 [0.690−0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model's prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823−0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

A 680,000-person megastudy of nudges to encourage vaccination in pharmacies.

Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most-effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top-performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was "waiting for you." Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy.

A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor's appointment.

Many Americans fail to get life-saving vaccines each year, and the availability of a vaccine for COVID-19 makes the challenge of encouraging vaccination more urgent than ever. We present a large field experiment (N = 47,306) testing 19 nudges delivered to patients via text message and designed to boost adoption of the influenza vaccine. Our findings suggest that text messages sent prior to a primary care visit can boost vaccination rates by an average of 5%. Overall, interventions performed better when they were 1) framed as reminders to get flu shots that were already reserved for the patient and 2) congruent with the sort of communications patients expected to receive from their healthcare provider (i.e., not surprising, casual, or interactive). The best-performing intervention in our study reminded patients twice to get their flu shot at their upcoming doctor's appointment and indicated it was reserved for them. This successful script could be used as a template for campaigns to encourage the adoption of life-saving vaccines, including against COVID-19.

Putting the Self in Self-Correction: Findings From the Loss-of-Confidence Project.

Science is often perceived to be a self-correcting enterprise. In principle, the assessment of scientific claims is supposed to proceed in a cumulative fashion, with the reigning theories of the day progressively approximating truth more accurately over time. In practice, however, cumulative self-correction tends to proceed less efficiently than one might naively suppose. Far from evaluating new evidence dispassionately and infallibly, individual scientists often cling stubbornly to prior findings. Here we explore the dynamics of scientific self-correction at an individual rather than collective level. In 13 written statements, researchers from diverse branches of psychology share why and how they have lost confidence in one of their own published findings. We qualitatively characterize these disclosures and explore their implications. A cross-disciplinary survey suggests that such loss-of-confidence sentiments are surprisingly common among members of the broader scientific population yet rarely become part of the public record. We argue that removing barriers to self-correction at the individual level is imperative if the scientific community as a whole is to achieve the ideal of efficient self-correction.

Can You Ever Be Too Smart for Your Own Good? Comparing Linear and Nonlinear Effects of Cognitive Ability on Life Outcomes.

Despite a long-standing expert consensus about the importance of cognitive ability for life outcomes, contrary views continue to proliferate in scholarly and popular literature. This divergence of beliefs presents an obstacle for evidence-based policymaking and decision-making in a variety of settings. One commonly held idea is that greater cognitive ability does not matter or is actually harmful beyond a certain point (sometimes stated as > 100 or 120 IQ points). We empirically tested these notions using data from four longitudinal, representative cohort studies comprising 48,558 participants in the United States and United Kingdom from 1957 to the present. We found that ability measured in youth has a positive association with most occupational, educational, health, and social outcomes later in life. Most effects were characterized by a moderate to strong linear trend or a practically null effect (mean R2 range = .002-.256). Nearly all nonlinear effects were practically insignificant in magnitude (mean incremental R2 = .001) or were not replicated across cohorts or survey waves. We found no support for any downside to higher ability and no evidence for a threshold beyond which greater scores cease to be beneficial. Thus, greater cognitive ability is generally advantageous-and virtually never detrimental.

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates.

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.

Measuring and Controlling for the Compromise Effect When Estimating Risk Preference Parameters.

The compromise effect arises when being close to the "middle" of a choice set makes an option more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it can bias researchers' inferences about preference parameters. To study this bias, we conduct an experiment with 550 participants who made choices over lotteries from multiple price lists (MPLs). Following prior work, we manipulate the compromise effect to influence choices by varying the middle options of each MPL. We then estimate risk preferences using a discrete-choice model without a compromise effect embedded in the model. As anticipated, the resulting risk preference parameter estimates are not robust, changing as the compromise effect is manipulated. To disentangle risk preference parameters from the compromise effect and to measure the strength of the compromise effect, we augment our discrete-choice model with additional parameters that represent a rising penalty for expressing an indifference point further from the middle of the ordered MPL. Using this method, we estimate an economically significant magnitude for the compromise effect and generate robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations.

Objecting to experiments even while approving of the policies or treatments they compare.

We resolve a controversy over two competing hypotheses about why people object to randomized experiments: 1) People unsurprisingly object to experiments only when they object to a policy or treatment the experiment contains, or 2) people can paradoxically object to experiments even when they approve of implementing either condition for everyone. Using multiple measures of preference and test criteria in five preregistered within-subjects studies with 1,955 participants, we find that people often disapprove of experiments involving randomization despite approving of the policies or treatments to be tested.