Exploring whether mental health crisis text conversations that include discussion of firearms differ from those without firearms.

BACKGROUND: Firearm violence represents a public health crisis in the United States. Yet, there is limited knowledge about how firearms are discussed in the context of mental health emergencies representing a major gap in the current research literature. This study addresses this gap by examining whether the content of mental health crisis text conversations that mention firearms differ from those that do not mention firearms in a large, unique dataset from a national crisis text line. METHODS: We examined data from over 3.2 million conversations between texters to Crisis Text Line and volunteer crisis counselors between September 2018 and July 2022. We used a study developed text classification machine learning algorithm that builds on natural language processing to identify and label whether crisis conversations mentioned firearms. We compared the frequency of psychosocial factors between conversations that mention firearms with those that did not. RESULTS: Results from a generalized linear mixed-effects model demonstrated that. conversations mentioning firearms more frequently were associated with suicide, racism, physical, sexual, emotional, and unspecified abuse, grief, concerns about a third party, substance use, bullying, gender and sexual identity, relationships, depression, and loneliness. Further, conversations mentioning firearms were less likely to be related to self-harm and eating/body image. CONCLUSIONS: These results offer an initial glimpse of how firearms are mentioned in the context of acute mental health emergencies, which has been completely absent in prior literature. Our results are preliminary and help sharpen our understanding of contextual factors surrounding mental health emergencies where a firearm is mentioned.

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country's existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as "residential" or "nonresidential" through visual inspection of aerial images. "Nonresidential" units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. RESULTS: On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. CONCLUSIONS: Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

BACKGROUND: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. OBJECTIVES: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. METHODS: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. RESULTS: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. CONCLUSIONS: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.

Effect of Paxlovid Treatment During Acute COVID-19 on Long COVID Onset: An EHR-Based Target Trial Emulation from the N3C and RECOVER Consortia.

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

The Use of Crisis Services Following the Mass School Shooting in Uvalde, Texas: Quasi-Experimental Event Study.

BACKGROUND: Mass shootings result in widespread psychological trauma for survivors and members of the affected community. However, less is known about the broader effects of indirect exposure (eg, media) to mass shootings. Crisis lines offer a unique opportunity to examine real-time data on the widespread psychological effects of mass shootings. OBJECTIVE: Crisis Text Line is a not-for-profit company that provides 24/7 confidential SMS text message-based mental health support and crisis intervention service. This study examines changes in the volume and composition of firearm-related conversations at Crisis Text Line before and after the mass school shooting at Robb Elementary School on May 24, 2022, in Uvalde, Texas. METHODS: A quasi-experimental event study design was used to compare the actual volume of firearm-related conversations received by Crisis Text Line post shooting to forecasted firearm conversation volume under the counterfactual scenario that a shooting had not occurred. Conversations related to firearms were identified among all conversations using keyword searches. Firearm conversation volume was predicted using a seasonal autoregressive integrated moving average model trained on the 3 months of data leading up to the shooting. Additionally, proportions of issue tags (topics coded post conversation by volunteer crisis counselors at Crisis Text Line after the exchange) were compared in the 4 days before (n=251) and after (n=417) the shooting to assess changes in conversation characteristics. The 4-day window was chosen to reflect the number of days conversation volume remained above forecasted levels. RESULTS: There was a significant increase in the number of conversations mentioning firearms following the shooting, with the largest spike (compared to forecasted numbers) occurring the day after the shooting (n=159) on May 25, 2022. By May 28, the volume reverted to within the 95% CI of the forecasted volume (n=77). Within firearm conversations, "grief" issue tags showed a significant increase in proportion in the week following the shooting, while "isolation/loneliness," "relationships," and "suicide" issue tags showed a significant decrease in proportions the week following the shooting. CONCLUSIONS: The results suggest that the Uvalde school shooting may have contributed to an increase in demand for crisis services, above what would be expected given historical trends. Additionally, we found that these firearm-related crises conversations immediately post event are more likely to be related to grief and less likely to be related to suicide, loneliness, and relationships. Our findings provide some of the first data showing the real-time repercussions for the broader population exposed to school shooting events. This work adds to a growing evidence base documenting and measuring the rippling effects of mass shootings outside of those directly impacted.

An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit.

Introduction: This study analyzes age-differentiated Reddit conversations about ENDS. Methods: This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded. Results: Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior. Conclusions: Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.

Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program.

Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID-a clinical diagnosis (n = 47,404) or a previously described computational phenotype (n = 198,514)-to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients' data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history.

Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C.

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Long COVID Risk and Pre-COVID Vaccination: An EHR-Based Cohort Study from the RECOVER Program.

Importance: Characterizing the effect of vaccination on long COVID allows for better healthcare recommendations. Objective: To determine if, and to what degree, vaccination prior to COVID-19 is associated with eventual long COVID onset, among those a documented COVID-19 infection. Design Settings and Participants: Retrospective cohort study of adults with evidence of COVID-19 between August 1, 2021 and January 31, 2022 based on electronic health records from eleven healthcare institutions taking part in the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, a project of the National Covid Cohort Collaborative (N3C). Exposures: Pre-COVID-19 receipt of a complete vaccine series versus no pre-COVID-19 vaccination. Main Outcomes and Measures: Two approaches to the identification of long COVID were used. In the clinical diagnosis cohort (n=47,752), ICD-10 diagnosis codes or evidence of a healthcare encounter at a long COVID clinic were used. In the model-based cohort (n=199,498), a computable phenotype was used. The association between pre-COVID vaccination and long COVID was estimated using IPTW-adjusted logistic regression and Cox proportional hazards. Results: In both cohorts, when adjusting for demographics and medical history, pre-COVID vaccination was associated with a reduced risk of long COVID (clinic-based cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; model-based cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75). Conclusions and Relevance: Long COVID has become a central concern for public health experts. Prior studies have considered the effect of vaccination on the prevalence of future long COVID symptoms, but ours is the first to thoroughly characterize the association between vaccination and clinically diagnosed or computationally derived long COVID. Our results bolster the growing consensus that vaccines retain protective effects against long COVID even in breakthrough infections. Key Points: Question: Does vaccination prior to COVID-19 onset change the risk of long COVID diagnosis?Findings: Four observational analyses of EHRs showed a statistically significant reduction in long COVID risk associated with pre-COVID vaccination (first cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; second cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75).Meaning: Vaccination prior to COVID onset has a protective association with long COVID even in the case of breakthrough infections.

Predicting age groups of Twitter users based on language and metadata features.

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.