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BACKGROUND: Selective Serotonin Reuptake Inhibitors (SSRIs) represent a diverse class of medications widely prescribed for depression and anxiety. Despite their common use, there is an absence of large-scale, real-world evidence capturing the heterogeneity in their effects on individuals. This study addresses this gap by utilizing naturalistic search data to explore the varied impact of six different SSRIs on user behavior. METHODS: The study sample included â¼508 thousand Bing users with searches for one of six SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline) from April-December 2022, comprising 510 million queries. Cox proportional hazard models were employed to examine 30 topics (e.g., shopping, tourism, health) and 195 health symptoms (e.g., anxiety, weight gain, impotence), using each SSRI as a reference. We assessed the relative hazard ratios between drugs and, where feasible, ranked the SSRIs based on their observed effects. We used Cox proportional hazard models in order to account for both the likelihood of users searching for a particular topic or symptom and the associated time to that search. The temporal aspect aided in distinguishing between potential symptoms of the disorder, short-term medication side effects, and later appearing side effects. RESULTS: Differences were found in search behaviors associated with each SSRI. E.g., fluvoxamine was associated with a significantly higher likelihood of searching weight gain compared to all other SSRIs (HRs 1.85-2.93). Searches following citalopram were associated with significantly higher rates of later impotence queries compared to all other SSRIs (HRs 5.11-7.76), except fluvoxamine. Fluvoxamine was associated with a significantly higher rate of health related searches than all other SSRIs (HRs 2.11-2.36). CONCLUSIONS: Our study reveals new insights into the varying SSRI impacts, suggesting distinct symptom profiles. This novel use of large-scale, naturalistic search data contributes to pharmacovigilance efforts, enhancing our understanding of intra-class variation among SSRIs, potentially uncovering previously unidentified drug effects.
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Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months). The current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N = 55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.
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Afecto , Anhedonia , Depresión , Trastorno Depresivo Mayor , Evaluación Ecológica Momentánea , Humanos , Anhedonia/fisiología , Masculino , Femenino , Adulto , Trastorno Depresivo Mayor/psicología , Afecto/fisiología , Depresión/psicología , Persona de Mediana Edad , Adulto Joven , Uso de la Marihuana/psicologíaRESUMEN
BACKGROUND: Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). METHODS: The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. RESULTS: Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. LIMITATIONS: Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. CONCLUSIONS: The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.
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Trastorno de Personalidad Limítrofe , Trastorno Depresivo Mayor , Evaluación Ecológica Momentánea , Humanos , Trastorno de Personalidad Limítrofe/psicología , Trastorno de Personalidad Limítrofe/epidemiología , Trastorno de Personalidad Limítrofe/diagnóstico , Femenino , Trastorno Depresivo Mayor/psicología , Trastorno Depresivo Mayor/epidemiología , Adulto , Masculino , Persona de Mediana Edad , Adulto Joven , Neuroticismo , Autoinforme , Escalas de Valoración Psiquiátrica , Depresión/psicología , Depresión/epidemiología , Estudios Longitudinales , Teorema de Bayes , Índice de Severidad de la Enfermedad , ComorbilidadRESUMEN
There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N=7,572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.
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Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Trastorno Depresivo Mayor , Humanos , Trastorno Depresivo Mayor/diagnóstico , Depresión , Proyectos de InvestigaciónRESUMEN
Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.
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Trastorno Depresivo Mayor , Dispositivos Electrónicos Vestibles , Humanos , Trastorno Depresivo Mayor/diagnóstico , Encuestas Nutricionales , Sueño , Actigrafía/métodosRESUMEN
Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.
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Trastorno Depresivo Mayor , Dispositivos Electrónicos Vestibles , Humanos , Depresión/diagnóstico , Depresión/epidemiología , Depresión/complicaciones , Trastorno Depresivo Mayor/diagnóstico , Trastorno Depresivo Mayor/epidemiología , ComorbilidadRESUMEN
Objective: Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal infection (PANDAS) and Pediatric Acute-Onset Neuropsychiatric syndrome (PANS) are presumed autoimmune complications of infection or other instigating events. To determine the incidence of these disorders, we performed a retrospective review for the years 2017-2019 at three academic medical centers. Methods: We identified the population of children receiving well-child care at each institution. Potential cases of PANS and PANDAS were identified by including children age 3-12 years at the time they received one of five new diagnoses: avoidant/restrictive food intake disorder, other specified eating disorder, separation anxiety disorder of childhood, obsessive-compulsive disorder, or other specified disorders involving an immune mechanism, not elsewhere classified. Tic disorders was not used as a diagnostic code to identify cases. Data were abstracted; cases were classified as PANDAS or PANS if standard definitions were met. Results: The combined study population consisted of 95,498 individuals. The majority were non-Hispanic Caucasian (85%), 48% were female and the mean age was 7.1 (SD 3.1) years. Of 357 potential cases, there were 13 actual cases [mean age was 6.0 (SD 1.8) years, 46% female and 100% non-Hispanic Caucasian]. The estimated annual incidence of PANDAS/PANS was 1/11,765 for children between 3 and 12 years with some variation between different geographic areas. Conclusion: Our results indicate that PANDAS/PANS is a rare disorder with substantial heterogeneity across geography and time. A prospective investigation of the same question is warranted.
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Background: With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings. Methods: We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model's performance. We used generalized linear mixed-effects models to quantify the relationship between demographic factors and model interpretation. Findings: We found variable model performance across diagnoses; attention deficit hyperactivity disorder, posttraumatic stress disorder, alcohol use disorder, narcissistic personality disorder, binge eating disorder, and generalized anxiety disorder showed high BAC (0.70 ≤ BAC ≤ 0.82); bipolar disorder, bulimia nervosa, barbiturate use disorder, conduct disorder, somatic symptom disorder, benzodiazepine use disorder, LSD use disorder, histrionic personality disorder, and functional neurological symptom disorder showed low BAC (BAC ≤ 0.59). Interpretation: Our findings demonstrate initial promise in the domain knowledge of a large AI model, with performance variability perhaps due to the more salient hallmark symptoms, narrower differential diagnosis, and higher prevalence of some disorders. We found limited evidence of model demographic bias, although we do observe some gender and racial differences in model outcomes mirroring real-world differential prevalence estimates.
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INTRODUCTION: Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population. METHODS: To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15-17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features. RESULTS: The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience. DISCUSSION: This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience.
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Aprendizaje Profundo , Humanos , Adolescente , Trastornos de Ansiedad/diagnóstico , Trastornos de Ansiedad/epidemiología , Ansiedad/diagnóstico , Ansiedad/epidemiología , Encuestas y CuestionariosRESUMEN
Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R2 = 0.42, p < 5.0 × 10-6). The results suggested that online screening was more prominent in underserved rural communities, therefore presenting as important tools with which to bridge mental health-care gaps in rural, resource-deficient areas.
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Salud Mental , Población Rural , Humanos , Estados Unidos , Autoinforme , Encuestas y Cuestionarios , Accesibilidad a los Servicios de SaludRESUMEN
Introduction: Despite existing work examining the effectiveness of smartphone digital interventions for schizophrenia at the group level, response to digital treatments is highly variable and requires more research to determine which persons are most likely to benefit from a digital intervention. Materials and methods: The current work utilized data from an open trial of patients with psychosis (N = 38), primarily schizophrenia spectrum disorders, who were treated with a psychosocial intervention using a smartphone app over a one-month period. Using an ensemble of machine learning models, pre-intervention data, app use data, and semi-structured interview data were utilized to predict response to change in symptom scores, engagement patterns, and qualitative impressions of the app. Results: Machine learning models were capable of moderately (r = 0.32-0.39, R2 = 0.10-0.16, MAE norm = 0.13-0.29) predicting interaction and experience with the app, as well as changes in psychosis-related psychopathology. Conclusion: The results suggest that individual smartphone digital intervention engagement is heterogeneous, and symptom-specific baseline data may be predictive of increased engagement and positive qualitative impressions of digital intervention in patients with psychosis. Taken together, interrogating individual response to and engagement with digital-based intervention with machine learning provides increased insight to otherwise ignored nuances of treatment response.
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INTRODUCTION: Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information). METHODS: The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes. RESULTS: We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability. LIMITATIONS: However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information. CONCLUSIONS: Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.
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Trastorno Depresivo Mayor , Esquizofrenia , Análisis por Conglomerados , Depresión , Trastorno Depresivo Mayor/diagnóstico , Humanos , Esquizofrenia/diagnóstico , Aprendizaje Automático no SupervisadoRESUMEN
Importance: Selective serotonin reuptake inhibitors (SSRIs) are a common first-line treatment for some psychiatric disorders, including depression and anxiety; although they are generally well tolerated, SSRIs have known adverse effects, including movement problems, sleep disruption, and gastrointestinal problems (eg, nausea and upset stomach). No large-scale studies using naturalistic, longitudinal, objective data have validated physical activity findings, and actigraphy data are well suited to address this task. Objectives: To evaluate whether differences in physical movement exist among individuals treated with SSRIs compared with control participants and to identify the unique features of the movement of patients treated with SSRIs. Design, Setting, and Participants: This cross-sectional study examines longitudinally collected wearable movement data within a cross-sectional sample of 7162 participants from the 2005-2006 National Health and Nutrition Examination Survey (NHANES), a nationally representative population-based sample of noninstitutionalized persons in the US having both medication information and passive movement data. Statistical analysis was performed from April 1, 2021, to February 1, 2022. Exposures: The use of SSRIs (sertraline hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, paroxetine hydrochloride, and citalopram hydrobromide), as reported by participants interviewed by NNHANES personnel, was the primary exposure, measured as a binary variable (taking an SSRI vs not taking an SSRI). Main Outcomes and Measures: The primary outcome was the intensity of body movement as recorded by a piezoelectric accelerometer worn on the right hip for more than 1 week. Results: Of the 7162 participants included in the study, the mean (SD) age was 33.7 (22.6) years, 266 (3.7%) were taking an SSRI, 3706 (51.7%) were female, 1934 (27.0%) were Black, 1823 (25.5%) were Mexican American, 210 (2.9%) were other Hispanic, 336 (4.7%) were other or multiracial, and 2859 (39.9%) were White (per the NHANES data collection protocol). A cross-validated, deep learning classifier was constructed that achieved fair performance predicting SSRI use (area under the curve, 0.67 [95% CI, 0.64-0.71] for the validation set and 0.66 [95% CI, 0.64-0.68] for the test set). To account for possible confounding by indication, we constructed a parallel model incorporating depression severity, finding only marginal performance improvement. When averaged across individuals and across 7 days, the results show less overall movement in the SSRI group (mean, 120.1 vertical acceleration counts/min [95% CI, 115.7-124.6 vertical acceleration counts/min]) compared with the non-SSRI control group (mean, 168.8 vertical acceleration counts/min [95% CI, 162.8-174.9 vertical acceleration counts/min]). Conclusions and Relevance: This cross-sectional study found a moderate association between passive movement and SSRI use, as well as SSRI detection capacity of passive movement using time series deep learning models. The results support the use of passive sensors for exploration and characterization of psychotropic medication adverse effects.
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Aprendizaje Profundo , Inhibidores Selectivos de la Recaptación de Serotonina , Acelerometría , Adulto , Estudios Transversales , Femenino , Humanos , Encuestas Nutricionales , Inhibidores Selectivos de la Recaptación de Serotonina/efectos adversosRESUMEN
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
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Trastornos de Ansiedad/epidemiología , Ansiedad/epidemiología , Depresión/epidemiología , Trastorno Depresivo Mayor/epidemiología , Adolescente , Adulto , Ansiedad/patología , Trastornos de Ansiedad/patología , Inteligencia Artificial , Depresión/patología , Trastorno Depresivo Mayor/patología , Registros Electrónicos de Salud , Femenino , Humanos , Aprendizaje Automático , Masculino , Atención Primaria de Salud , Escalas de Valoración Psiquiátrica , Encuestas y Cuestionarios , Adulto JovenRESUMEN
BACKGROUND: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people's everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. OBJECTIVE: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. METHODS: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. RESULTS: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. CONCLUSIONS: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.
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In this study, we aim to develop an automatic pipeline to identify clinical findings in the unstructured text of radiology reports that necessitate communications between radiologists and referring physicians. Our approach identified 20 distinct clinical concepts and highlighted statistically significant concepts with strong associations to cases that require prompt communication.