Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data

BACKGROUND

Antidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants.

OBJECTIVE

We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.

METHODS

On a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018.

RESULTS

Five major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects.

CONCLUSIONS

This work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants.

Evaluating evaluation frameworks: a scoping review of frameworks for assessing health apps

OBJECTIVES

Despite an estimated 300 000 mobile health apps on the market, there remains no consensus around helping patients and clinicians select safe and effective apps. In 2018, our team drew on existing evaluation frameworks to identify salient categories and create a new framework endorsed by the American Psychiatric Association (APA). We have since created a more expanded and operational framework Mhealth Index and Navigation Database (MIND) that aligns with the APA categories but includes objective and auditable questions (105). We sought to survey the existing space, conducting a review of all mobile health app evaluation frameworks published since 2018, and demonstrate the comprehensiveness of this new model by comparing it to existing and emerging frameworks.

DESIGN

We conducted a scoping review of mobile health app evaluation frameworks.

DATA SOURCES

References were identified through searches of PubMed, EMBASE and PsychINFO with publication date between January 2018 and October 2020.

ELIGIBILITY CRITERIA

Papers were selected for inclusion if they meet the predetermined eligibility criteria-presenting an evaluation framework for mobile health apps with patient, clinician or end user-facing questions.

DATA EXTRACTION AND SYNTHESIS

Two reviewers screened the literature separately and applied the inclusion criteria. The data extracted from the papers included: author and dates of publication, source affiliation, country of origin, name of framework, study design, description of framework, intended audience/user and framework scoring system. We then compiled a collection of more than 1701 questions across 79 frameworks. We compared and grouped these questions using the MIND framework as a reference. We sought to identify the most common domains of evaluation while assessing the comprehensiveness and flexibility-as well as any potential gaps-of MIND.

RESULTS

New app evaluation frameworks continue to emerge and expand. Since our 2019 review of the app evaluation framework space, more frameworks include questions around privacy (43) and clinical foundation (57), reflecting an increased focus on issues of app security and evidence base. The majority of mapped frameworks overlapped with at least half of the MIND categories. The results of this search have informed a database (apps.digitalpsych.org) that users can access today.

CONCLUSION

As the number of app evaluation frameworks continues to rise, it is becoming difficult for users to select both an appropriate evaluation tool and to find an appropriate health app. This review provides a comparison of what different app evaluation frameworks are offering, where the field is converging and new priorities for improving clinical guidance.

Investigating Associations Between Screen Time and Symptomatology in Individuals With Serious Mental Illness: Longitudinal Observational Study

BACKGROUND

Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness.

OBJECTIVE

To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis.

METHODS

In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising. Age-matched, smartphone-owning healthy controls were also part of the recruitment pool. A specification curve analysis was a priori designed to explore the relationship between every combination of independent variable and dependent variable in order to demonstrate to what degree screen time relates to symptoms in individuals with serious mental illness.

RESULTS

The sample consisted of 88 participants (54 with schizophrenia and 34 healthy controls) who completed both the initial and follow-up visits, completed at least one self-reported survey, and had a minimum passive data cutoff of 5 consecutive days. While we found an association between smartphone screen time metrics and cognition (adjusted R²=0.107, P<.001), specification curve analysis revealed a wide range of heterogenous associations with screen time from very negative to very positive. The effects differed based on diagnostic group, age bracket, type of regression model used, and the specific independent and dependent variables selected for analysis.

CONCLUSIONS

The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.

Association of Patients Reading Clinical Notes With Perception of Medication Adherence Among Persons With Serious Mental Illness

This survey study examines how patients with a mental illness diagnosis who read at least 1 clinical note in the last year perceived its association with their medication adherence.

Child and adolescent asynchronous technology competencies for clinical care and training: Scoping review

Objective: Asynchronous technologies such as mobile health, e-mail, e-consult, and social media are being added to in-person and synchronous service delivery. To ensure quality care, clinicians need skills, knowledge, and attitudes related to technology that can be measured. This study sought out competencies for asynchronous technologies and/or an approach to define them. Methods: This 6-stage scoping review of Pubmed/Medline, APA PsycNET, PsycINFO and other databases was based on a broad research question, "What skills are needed for clinicians and trainees to provide quality care using asynchronous technologies for children and adolescents, and how can they be made measurable to implement, teach and evaluate?" The search focused on key words in 4 concept areas: (a) competencies; (b) asynchronous technology; (c) synchronous telepsychiatry, telebehavioral or telemental health; and (d) clinical. The screeners reviewed the full-text articles based on inclusion (mesh of the key words) and exclusion criteria. Results: From a total of 5,877 potential references, 2 authors found 509 eligible for full text review and found 110 articles directly relevant to the concepts. Clinical studies discuss clinical, technical and administrative workflow rather than competencies, though behavioral health professions' position statements advise on adapting care and training. Existing technology competencies for video, social media, mobile health, and other asynchronous technologies were used to build a framework. Training, faculty development, and organizational suggestions are suggested. Conclusions: Research is needed on how to implement and evaluate asynchronous competencies to ensure quality clinical care and training, which is a paradigm shift for participants. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Marketplace and Literature Review of Spanish Language Mental Health Apps

Language differences between patients and providers remains a barrier to accessing health care, especially mental health services. One potential solution to reduce inequities for patients that speak different languages and improve their access to care is through the delivery of healthcare through mobile technology. Given that the Latinx community serves as the largest ethnic minority in the United States, this two-phased review examines Spanish app development, feasibility and efficacy. Phase 1 explored the commercial marketplace for apps available in Spanish, while phase 2 involved a literature review of published research centered around the creation, functions, and usability of these apps using the PubMed and Google Scholar electronic databases. Of the apps available on the database, only 14.5% of them had Spanish operability. The literature search uncovered 629 results, of which 12 research articles that tested or described 10 apps met the inclusion criteria. Of the 10 apps studied in this literature review, only four apps were translated to Spanish. Our study reveals that despite increasing interest in Spanish-language apps to address mental health, the commercial marketplace is not currently meeting the demand.

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.

ReMindCare, an app for daily clinical practice in patients with first episode psychosis: A pragmatic real-world study protocol

AIM

Despite the potential benefits of e-health interventions for patients with psychosis, the integration of these applications into the clinical workflow and analysis of their long-term effects still face significant challenges. To address these issues, we developed the ReMindCare app. This app aims to improve the treatment quality for patients with psychosis. We chose to study the app in real world and pragmatic manner to ensure results will be generalizable.

METHODS

This is a naturalistic empirical study of patients in a first episode of psychosis programme. The app was purpose-designed based on two previous studies, and it offers the following assessments: (a) three daily questions regarding anxiety, sadness and irritability; and (b) 18 weekly questions about medication adherence, medication side effects, medication attitudes and prodromal symptoms. The app offers preset alerts, reminders and the ability for patients to reach out to their clinicians. Data captured by the app are linked to the electronic medical record of the patient. Patients will use the app as part of their ongoing care for a maximum period of 5 years, and assessments will occur at baseline and at the end of the first, second and fifth years of app use.

RESULTS

Recruitment started in October 2018 and is still ongoing.

CONCLUSIONS

The ReMindCare app represents early real-world use of digital mental health tools that offer direct integration into clinical care. High retention and compliance rates are expected, and this will in turn lead to improved quality of assessments and communication between patients and clinicians.

Psychiatric rehabilitation through teaching smartphone skills to improve functional outcomes in serious mental illness

This study measured the impact of a digital competencies and skills course on participants with serious mental illness. Close to 75% of participants reported an improvement in a smartphone related skill, and the majority of participants that reported improvement in one skill reported improvement in at least one other. Qualitative feedback from participants suggests how digital competencies acquired were used to immediately support functional outcomes.

OBJECTIVE

To improve functional outcomes in patients with serious mental illness through a multi-session curriculum designed to improve smartphone skills and engage participants in group learning and problem solving, targeting negative and cognitive symptoms of illness.

METHODS

An eight-week smartphone digital competencies and skills course was offered to two distinct groups of youth with serious mental illness. Pre and post self-report measurements were captured for each participant for each session.

RESULTS

Group participation varied by session, but overall 28 unique patients attended. From survey results, 75% reported improvement in smartphone related skills because of the groups. Qualitative feedback suggests how skills acquired by patients were immediately utilized to gain insight into health and support functional outcomes.

CONCLUSIONS AND IMPLICATIONS FOR PRACTICE

Smartphone skills groups are a means to provide practical psychiatric rehabilitation that may enable some patients to compensate for cognitive and social deficits due to illness. While ensuring groups are responsive to patients with varying degrees of skills remains a challenge, adapting lesson structures and mediums, as well as creating new measurement tools, offers a means to modify the course with the clinical need.

Technology Enabled Clinical Care (TECC): Protocol for a Prospective Longitudinal Cohort Study of Smartphone-Augmented Mental Health Treatment

BACKGROUND

Even before COVID-19, there has been an urgent need to expand access to and quality of mental health care. This paper introduces an 8-week treatment protocol to realize that vision-Technology Enabled Clinical Care (TECC). TECC offers innovation in clinical assessment, monitoring, and interventions for mental health. TECC uses the mindLAMP app to enable digital phenotyping, clinical communication, and smartphone-based exercises that will augment in-person or telehealth virtual visits. TECC exposes participants to an array of evidence-based treatments (cognitive behavioral therapy, dialectical behavior therapy, acceptance and commitment therapy) introduced through clinical sessions and then practiced through interactive activities provided through a smartphone app called mindLAMP.

OBJECTIVE

TECC will test the feasibility of providing technology-enabled mental health care within an outpatient clinic; explore the practicality for providing this care to individuals with limited English proficiency; and track anxiety, depression, and mood symptoms for participants to measure the effectiveness of the TECC design.

METHODS

The TECC study will assess the acceptability and efficacy of this care model in 50 participants as compared to an age- and gender-matched cohort of patients presenting with similar clinical severity of depression, anxiety, or psychotic symptoms. Participants will be recruited from clinics in the Metro Boston area. Aspects of TECC will be conducted in both Spanish and English to ensure wide access to care for multiple populations.

RESULTS

The results of the TECC study will be used to support or adapt this model of care and create training resources to ensure its dissemination. The study results will be posted on ClinicalTrials.gov, with primary outcomes related to changes in mood, anxiety, and stress, and secondary outcomes related to engagement, alliance, and satisfaction.

CONCLUSIONS

TECC combines new digital mental health technology with updated clinical protocols and workflows designed to ensure patients can benefit from innovation in digital mental health. Supporting multiple languages, TECC is designed to ensure digital health equity and highlights how mobile health can bridge, not expand, gaps in care for underserved populations.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/23771.

Anomaly detection to predict relapse risk in schizophrenia

The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.

Smartphone Health Assessment for Relapse Prevention (SHARP): a digital solution toward global mental health

BACKGROUND

Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients' symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals.

AIMS

Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness.

METHOD

SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2.

RESULTS

The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health.

CONCLUSIONS

The digital tools developed as a result of this study, and participants' experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.

Exploring the Neuropsychiatric Sequalae of Perceived COVID-19 Exposure in College Students: A Pilot Digital Phenotyping Study

The urgency to understand the long-term neuropsychiatric sequala of COVID-19, a part of the Post-Acute COVID-19 Syndrome (PACS), is expanding as millions of infected individuals experience new unexplained symptoms related to mood, anxiety, insomnia, headache, pain, and more. Much research on PACS involves cross sectional surveys which limits ability to understand the dynamic trajectory of this emerging phenomenon. In this secondary analysis, we analyzed data from a 4-week observational digital phenotyping study using the mindLAMP app for 695 college students with elevated stress who specified if they were exposed to COVID-19. Students also completed a biweekly survey of clinical assessments to obtain active data. Additionally, passive data streams like GPS, accelerometer, and screen state were extracted from phone sensors and through features the group built. Three hundred and eighty-second number participants successfully specified their COVID-19 exposure and completed the biweekly survey. From active smartphone data, we found significantly higher scores for the Prodromal Questionnaire (PQ) and the Pittsburgh Sleep Quality Index (PSQI) for students reporting exposure to COVID-19 compared to those who were not (ps < 0.05). Additionally, we found significantly decreased sleep duration as captured from the smartphone via passive data for the COVID-19 exposed group (p < 0.05). No significant differences were detected for other surveys or passive sensors. Smartphones can capture both self-reported symptoms and behavioral changes related to PACS. Our results around changes in sleep highlight how digital phenotyping methods can be used in a scalable and accessible manner toward better capturing the evolving phenomena of PACS. The present study further provides a foundation for future research to implement improving digital phenotyping methods.

Exploring the Association Between Electronic Wearable Device Use and Levels of Physical Activity Among Individuals With Depression and Anxiety: A Population Level Study

Aim: The present study aimed to investigate the cross-sectional association between self-reported use of electronic wearable devices (EWDs) and the levels of physical activity among a representative sample of adults with depression and anxiety in the United States. Methods: For this cross-sectional study, data were pooled from the Health Information National Trends Survey 2019. A sample of 1,139 adults with self-reported depression and anxiety (60.9% women; mean age of 52.5 years) was analyzed. The levels of physical activity and prevalence of EWD utilization were self-reported. The chi-square tests were used to compare individual characteristics through the use of EWDs. Multivariable logistic regression was employed to investigate the association between EWDs and physical activity levels while adjusting for sociodemographic and health-related factors. Results: From the 1,139 adults with self-reported depression and anxiety, 261 (weighted percentage 28.1%) endorsed using EWD in the last year. After adjusting for covariates, the use of EWDs was only significantly associated with a higher odds of reporting intention to lose weight (OR 2.12; 95% CI 1.04, 4.35; p = 0.04). We found no association between the use of EWDs and meeting the national weekly recommendation for physical activity or resistance/strength exercise training. Conclusion: About three in 10 adults suffering from depression and anxiety in the United States reported using EWDs in the last year. The current study findings indicate that among people living with mental illness, EWD use is associated with higher odds of weight loss intent suggesting that EWDs may serve as an opening for the clinical interactions around physical health through identifying patients primed for behavior change. Further large-scale studies using randomized trial designs are needed to examine the causal relationships between EWDs and the physical activity of people with mental health conditions.

Social decline in the psychosis prodrome: Predictor potential and heterogeneity of outcome

BACKGROUND

While an established clinical outcome of high importance, social functioning has been emerging as possibly having a broader significance to the evolution of psychosis and long term disability. In the current study we explored the association between social decline, conversion to psychosis, and functional outcome in individuals at clinical high risk (CHR) for psychosis.

METHODS

585 subjects collected in the North American Prodrome Longitudinal Study (NAPLS2) were divided into 236 Healthy Controls (HCs), and CHR subjects that developed psychosis (CHR + C, N = 79), or those that did not (Non-Converters, CHR-NC, N = 270). CHR + C subjects were further divided into those that experienced an atypical decline in social functioning prior to baseline (beyond typical impairment levels) when in min-to-late adolescence (CHR + C-SD, N = 39) or those that did not undergoing a decline (CHR + C-NSD, N = 40).

RESULTS

Patterns of poor functional outcomes varied across the CHR subgroups: CHR-NC (Poor Social 36.3%, Role 42.2%) through CHR + C-NSD (Poor Social 50%, Poor Role 67.5%) to CHR + C-SD (Poor Social 76.9%, Poor Role 89.7%) functioning. The two Converter subgroups had comparable positive symptoms at baseline. At 12 months, the CHR + C-SD group stabilized, but social functioning levels remained significantly lower than the other two subgroups.

CONCLUSIONS

The current study demonstrates that pre-baseline social decline in mid-to-late adolescence predicts psychosis. In addition, we found that this social decline in converters is strongly associated with especially poor functional outcome and overall poorer prognosis. Role functioning, in contrast, has not shown similar predictor potential, and rather appears to be an illness indicator that worsens over time.

The Functionality, Evidence, and Privacy Issues Around Smartphone Apps for the Top Neuropsychiatric Conditions

OBJECTIVE

There are more than 325,000 health-related smartphone applications (apps) on the market. To better understand the apps currently on the market for the five most disabling neuropsychiatric conditions, the authors conducted a study investigating their intended uses (target population and intervention), the data collected, and any privacy policies.

METHODS

This was a cross-sectional study of apps for the five most disabling neuropsychiatric conditions per the World Health Organization: stroke, migraine, depression, Alzheimer's disease and dementia, and anxiety. Up to 15 apps in the U.S. Google Play and Apple app stores were selected based on the following prespecified inclusion criteria: the app appeared in the top 50 search results, offered intervention or tracking capabilities, and listed the condition in the app title or description. Exclusion criteria were <$5.00 to purchase, solely motor versus cognitive-based intervention, or designed for use by caregivers or health care providers. Data abstracted included function, behavior change rewards, and information about intervention, privacy policy, and payment.

RESULTS

Eighty-three apps were reviewed (stroke, N=8; migraine, N=25; Alzheimer's disease and dementia, N=8; depression, N=7; anxiety, N=14; apps targeting depression and anxiety, N=21). Sixty-nine percent of apps had an intervention component, 18% were deemed evidence based, 77% had a privacy policy, 70% required payment for access to all features, and 19% rewarded user behavior changes.

CONCLUSIONS

Most apps on the market targeted migraine, depression, and anxiety and contained interventions, although most of the interventions did not appear to be evidence based. Additionally, although most apps had privacy policies, lay people may have difficulty understanding these policies due to their complexities.

A systematic review of mHealth application interventions for peripartum mood disorders: trends and evidence in academia and industry

In this review, we aim to summarize research findings and marketplace apps for women with perinatal mood disorders with the goal of informing clinicians and patients about current risks and benefits, as well as proposing clinical implementation advice and a harmonized agenda for both academic and industry advancement in this space. Multiple searches were run of academic databases in 2018-2020, examining literature on mobile apps for peripartum mental health. Multiple searches were also run of the iOS and Android app stores in 2019 and 2020, looking at apps for peripartum mental health. Results were compared within the academic dataset as well within the commercial app dataset; the two datasets were also examined for overlap. The academic search results were notable for small sample sizes and heterogeneous endpoints. The app store search results were notable for apps of generally poor quality (as assessed by a modified Silberg scale). Very few of the mHealth interventions studied in the academic literature were available in the app store; very few of the apps from the commercial stores were supported by academic literature. The disconnect between academically developed apps and commercially available apps highlights the need for better collaboration between academia and industry. More collaboration between the two approaches may benefit both app developers and patients in this demographic moving forwards. Additionally, we present a set of practice guidelines for mHealth in perinatal psychiatry based on the trends identified in this review.

Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit

BACKGROUND

Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis.

METHOD

Response to points of critique.

RESULTS

We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma.

DISCUSSION

Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

Genome-wide association analysis of opioid use disorder: A novel approach using clinical data

BACKGROUND

Opioid use disorder (OUD) represents a large and pervasive global public health challenge. Previous genetic studies have demonstrated the significant heritability of OUD and identified several single-nucleotide polymorphisms (SNPs) associated with its prevalence.

METHODS

In this paper, we conducted a genome-wide association analysis on opioid use disorder that leveraged genetic and clinical data contained in a biobank of 21,310 patients of European ancestry. We identified 1039 cases of opioid use disorder based on diagnostic codes from nearly 16 million encounters in electronic health records (EHRs).

RESULTS

We discovered one novel OUD-associated locus on chromosome 4 that was significant at a genome-wide threshold (p = 2.40 × 10^-8). Heritability analysis suggested that common SNPs explained 0.06 (se 0.02, p = 0.0065) of the phenotypic variation in OUD. When we restricted controls to those with previous opioid prescriptions, we were able to further strengthen the original signal and discovered another significant locus on chromosome 16. Pair-wise genetic correlation analysis yielded strong positive correlations between OUD and two other major substance use disorders, alcohol and nicotine, with the strongest correlation between nicotine and opioid use disorder (genetic correlation 0.65, se = 0.19, p = 0.00048), suggesting a significant shared genetic component across different substance disorders.

CONCLUSIONS

This pragmatic, clinically-focused approach may supplement more traditional methods to facilitate identification of new genetic underpinnings of OUD and related disorders.