Are Home Evictions Associated with Child Welfare System Involvement? Empirical Evidence from National Eviction Records and Child Protective Services Data.

This study aimed to understand the relationship between home eviction and child welfare system involvement at the county level. Using administrative data, we examined associations of home eviction and eviction filing rates with child abuse and neglect (CAN) reports and foster care entries. We found one additional eviction per 100 renter-occupied homes in a county was associated with a 1.3% increase in the rate of CAN reports and a 1.6% increase in foster care entries. The association between eviction and foster care entries was strongest among Hispanic children with an 8.1% increase. Assisting parents in providing stable housing may reduce the risk of child welfare system involvement, including out-of-home child placement. Primary and secondary prevention strategies could include housing assistance, increasing access to affordable and safe housing, as well as providing economic support for families (e.g., tax credits, childcare subsidies) that reduce parental financial burden to access stable housing.

Using Transformer-Based Topic Modeling to Examine Discussions of Delta-8 Tetrahydrocannabinol: Content Analysis.

BACKGROUND: Delta-8 tetrahydrocannabinol (THC) is a psychoactive cannabinoid found in small amounts naturally in the cannabis plant; it can also be synthetically produced in larger quantities from hemp-derived cannabidiol. Most states permit the sale of hemp and hemp-derived cannabidiol products; thus, hemp-derived delta-8 THC products have become widely available in many state hemp marketplaces, even where delta-9 THC, the most prominently occurring THC isomer in cannabis, is not currently legal. Health concerns related to the processing of delta-8 THC products and their psychoactive effects remain understudied. OBJECTIVE: The goal of this study is to implement a novel topic modeling approach based on transformers, a state-of-the-art natural language processing architecture, to identify and describe emerging trends and topics of discussion about delta-8 THC from social media discourse, including potential symptoms and adverse health outcomes experienced by people using delta-8 THC products. METHODS: Posts from January 2008 to December 2021 discussing delta-8 THC were isolated from cannabis-related drug forums on Reddit (Reddit Inc), a social media platform that hosts the largest web-based drug forums worldwide. Unsupervised topic modeling with state-of-the-art transformer-based models was used to cluster posts into topics and assign labels describing the kinds of issues being discussed with respect to delta-8 THC. Results were then validated by human subject matter experts. RESULTS: There were 41,191 delta-8 THC posts identified and 81 topics isolated, the most prevalent being (1) discussion of specific brands or products, (2) comparison of delta-8 THC to other hemp-derived cannabinoids, and (3) safety warnings. About 5% (n=1220) of posts from the resulting topics included content discussing health-related symptoms such as anxiety, sleep disturbance, and breathing problems. Until 2020, Reddit posts contained fewer than 10 mentions of delta-8-THC for every 100,000 cannabis posts annually. However, in 2020, these rates increased by 13 times the 2019 rate (to 99.2 mentions per 100,000 cannabis posts) and continued to increase into 2021 (349.5 mentions per 100,000 cannabis posts). CONCLUSIONS: Our study provides insights into emerging public health concerns around delta-8 THC, a novel substance about which little is known. Furthermore, we demonstrate the use of transformer-based unsupervised learning approaches to derive intelligible topics from highly unstructured discussions of delta-8 THC, which may help improve the timeliness of identification of emerging health concerns related to new substances.

Recovery from Amblyopia in Adulthood: A Meta-Analysis.

Purpose: The effectiveness of traditional amblyopia therapies is largely restricted to childhood. However, recovery in adulthood is possible following removal or vision-limiting disease of the fellow eye. Study of this phenomenon is currently limited to isolated case reports and a few case series, with reported incidence ranging from 19-77% 1-5 . We set out to accomplish two distinct goals: (1) define the incidence of clinically meaningful recovery and (2) elucidate the clinical features associated with greater amblyopic eye gains. Methods: A systematic review of 3 literature databases yielded 23 reports containing 109 cases of patients ≥18 years old with unilateral amblyopia and vision-limiting fellow eye pathology. Results: Study 1 revealed 25/42 (59.5%) of adult patients gained ≥2 logMAR lines in the amblyopia eye after FE vision loss. The overall degree of improvement is clinically meaningful (median 2.6 logMAR lines). Study 2 showed that for cases with amblyopic eye visual acuity improvement, recovery occurs within 12 months of initial loss of fellow eye vision. Regression analysis revealed that younger age, worse baseline acuity in the amblyopic eye, and worse vision in the fellow eye independently conferred greater gains in amblyopic eye visual acuity. Recovery occurs across amblyopia types and fellow eye pathologies, although disease entities affecting fellow eye retinal ganglion cells demonstrate shorter latencies to recovery. Conclusions: Amblyopia recovery after fellow eye injury demonstrates that the adult brain harbors the neuroplastic capacity for clinically meaningful recovery, which could potentially be harnessed by novel approaches to treat adults with amblyopia.

Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time.

Importance: Firearm homicides are a major public health concern; lack of timely mortality data presents considerable challenges to effective response. Near real-time data sources offer potential for more timely estimation of firearm homicides. Objective: To estimate near real-time burden of weekly and annual firearm homicides in the US. Design, Setting, and Participants: In this prognostic study, anonymous, longitudinal time series data were obtained from multiple data sources, including Google and YouTube search trends related to firearms (2014-2019), emergency department visits for firearm injuries (National Syndromic Surveillance Program, 2014-2019), emergency medical service activations for firearm-related injuries (biospatial, 2014-2019), and National Domestic Violence Hotline contacts flagged with the keyword firearm (2016-2019). Data analysis was performed from September 2021 to September 2022. Main Outcomes and Measures: Weekly estimates of US firearm homicides were calculated using a 2-phase pipeline, first fitting optimal machine learning models for each data stream and then combining the best individual models into a stacked ensemble model. Model accuracy was assessed by comparing predictions of firearm homicides in 2019 to actual firearm homicides identified by National Vital Statistics System death certificates. Results were also compared with a SARIMA (seasonal autoregressive integrated moving average) model, a common method to forecast injury mortality. Results: Both individual and ensemble models yielded highly accurate estimates of firearm homicides. Individual models' mean error for weekly estimates of firearm homicides (root mean square error) varied from 24.95 for emergency department visits to 31.29 for SARIMA forecasting. Ensemble models combining data sources had lower weekly mean error and higher annual accuracy than individual data sources: the all-source ensemble model had a weekly root mean square error of 24.46 deaths and full-year accuracy of 99.74%, predicting the total number of firearm homicides in 2019 within 38 deaths for the entire year (compared with 95.48% accuracy and 652 deaths for the SARIMA model). The model decreased the time lag of reporting weekly firearm homicides from 7 to 8 months to approximately 6 weeks. Conclusions and Relevance: In this prognostic study of diverse secondary data on machine learning, ensemble modeling produced accurate near real-time estimates of weekly and annual firearm homicides and substantially decreased data source time lags. Ensemble model forecasts can accelerate public health practitioners' and policy makers' ability to respond to unanticipated shifts in firearm homicides.

Using the Centers for Disease Control and Prevention's National Syndromic Surveillance Program Data to Monitor Trends in US Emergency Department Visits for Firearm Injuries, 2018 to 2019.

STUDY OBJECTIVE: We describe trends in emergency department (ED) visits for initial firearm injury encounters in the United States. METHODS: Using data from the Centers for Disease Control and Prevention's National Syndromic Surveillance Program, we analyzed monthly and yearly trends in ED visit rates involving a firearm injury (calculated as the number of firearm injury-related ED visits divided by the total number of ED visits for each month and multiplied by 100,000) by sex-specific age group and US region from 2018 to 2019 and conducted Joinpoint regression to detect trend significance. RESULTS: Among approximately 215 million ED visits captured in the National Syndromic Surveillance Program from January 2018 to December 2019, 132,767 involved a firearm injury (61.6 per 100,000 ED visits). Among males, rates of firearm injury-related ED visits significantly increased for all age groups between 15 and 64 years during the study period. Among females, rates of firearm injury-related ED visits significantly increased for all age groups between 15 and 54 years during the study period. By region, rates significantly changed in the northeast, southeast, and southwest for males and females during the study period. CONCLUSION: These analyses highlight a novel data source for monitoring trends in ED visits for firearm injuries. With increased and effective use of state and local syndromic surveillance data, in addition to improvements to firearm injury syndrome definitions by intent, public health professionals could better detect unusual patterns of firearm injuries across the United States for improved prevention and tailored response efforts.

"I did it without hesitation. Am I the bad guy?" Online conversations in response to controversial in-game violence.

Video game content has evolved over the last six decades, from a basic focus on challenge and competition to include more serious and introspective narratives capable of encouraging critical contemplation within gamers. The "No Russian" mission from Call of Duty: Modern Warfare 2 casts players as terrorists responsible for the murder of innocent bystanders, sparking debate around how players engage and react to wanton violence in modern video games. Through thematic analysis of 649 Reddit posts discussing the mission, 10 themes emerged representing complexity in player experiences. Those themes were grouped into categories representing (descending order), (1) rote gameplay experiences, (2) dark humor, (3) comparing the mission to other games and real-world events, and (4) self-reflective eudaimonic reactions to the mission. Although less common, the presence of eudaimonic media effects (in at least 15% of posts) holds promise for the use of video games as reflective spaces for violence prevention.

Business and property types experiencing excess violent crime: a micro-spatial analysis.

BACKGROUND: Beyond alcohol retail establishments, most business and property types receive limited attention in studies of violent crime. We sought to provide a comprehensive examination of which properties experience the most violent crime in a city and how that violence is distributed throughout a city. METHODS: For a large urban city, we merged violent incident data from police reports with municipal tax assessor data from 2012-2017 and tabulated patterns of violent crime for 15 commercial and public property types. To describe outlier establishments, we calculated the proportion of individual parcels within each property-type that experienced more than 5 times the average number of crimes for that property-type and also mapped the 25 parcels with the highest number of violent incidents to explore what proportion of violent crime in these block groups were contributed by the outlier establishments. RESULTS: While the hotel/lodging property-type experienced the highest number of violent crimes per parcel (2.72), each property-type had outlier establishments experiencing more than 5 times the average number of violent crimes per business. Twelve of 15 property-types (80%) had establishments with more than 10 times the mean number of violent incidents. The 25 parcels with the most violent crime comprised a wide variety of establishments, ranging from a shopping center, grocery store, gas station, motel, public park, vacant lot, public street, office building, transit station, hospital, pharmacy, school, community center, and movie theatre, and were distributed across the city. Eight of the 25 parcels with the highest amount of violent crime, accounted for 50% or more of the violent crime within a 400-meter buffer. CONCLUSIONS: All property-types had outlier establishments experiencing elevated counts of violent crimes. Furthermore, the 25 most violent properties in the city demonstrated remarkable diversity in property-type. Further studies assessing the risk of violent crime among additional property-types may aid in violence prevention.

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

Importance: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. Objective: To estimate weekly suicide fatalities in the US in near real time. Design, Setting, and Participants: This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017. Exposures: Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017). Main Outcomes and Measures: Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System. Results: Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%). Conclusions and Relevance: The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.

Conversational topics of social media messages associated with state-level mental distress rates.

Background: Upstream public health indicators of poor mental health in the United States (U.S.) are currently measured by national telephone-based surveys; however, results are delayed by 1-2 years, limiting real-time assessment of trends.Aim: The aim of this study was to evaluate associations between conversational topics on Twitter from 2018 to 2019 and mental distress rates from 2017 to 2018 for the 50 U.S. states and capital.Method: We used a novel lexicon, Empath, to examine conversational topics from aggregate social media messages from Twitter that correlated most strongly with official U.S. state-level rates of mental distress from the Behavioral Risk Factor Surveillance System.Results: The ten lexical categories most positively correlated with rates of frequent mental distress at the state-level included categories about death, illness, or injury. Lexical categories most inversely correlated with mental distress included categories that serve as proxies for economic prosperity and industry. Using the prevalence of the 10 most positively and 10 most negatively correlated lexical categories to predict state-level rates of mental distress via a linear regression model on an independent sample of data yielded estimates that were moderately similar to actual rates (mean difference = 0.52%; Pearson correlation = 0.45, p < 0.001).Conclusion: This work informs efforts to use social media to measure population-level trends in mental health.

Syndromic Surveillance of Suicidal Ideation and Self-Directed Violence - United States, January 2017-December 2018.

Suicide is a growing public health problem in the United States, claiming approximately 47,000 lives in 2017 (1). However, deaths from suicide represent only a small part of a larger problem because each year millions of persons experience suicidal ideation and engage in suicidal and nonsuicidal self-directed violence, both risk factors for suicide (2). Emergency departments (EDs) are an important setting for monitoring these events in near real time (3-5). From 2001 to 2016, ED visit rates for nonfatal self-harm increased 42% among persons aged ≥10 years (1). Using data from CDC's National Syndromic Surveillance Program (NSSP), ED visits for suicidal ideation, self-directed violence, or both among persons aged ≥10 years during January 2017-December 2018 were examined by sex, age group, and U.S. region. During the 24-month period, the rate of ED visits for suicidal ideation, self-directed violence, or both increased 25.5% overall, with an average increase of 1.2% per month. Suicide prevention requires comprehensive and multisectoral approaches to addressing risk at personal, relationship, community, and societal levels. ED syndromic surveillance data can provide timely trend information and can support more targeted and prompt public health investigation and response. CDC's Preventing Suicide: A Technical Package of Policy, Programs, and Practices includes tailored suicide prevention strategies for health care settings (6).

Factors Associated With Increased Dissemination of Positive Mental Health Messaging On Social Media.

Background: The dissemination of positive messages about mental health is a key goal of organizations and individuals. Aims: Our aim was to examine factors that predict increased dissemination of such messages. Method: We analyzed 10,998 positive messages authored on Twitter and studied factors associated with messages that are shared (re-tweeted) using logistic regression. Characteristics of the account, message, linguistic style, sentiment, and topic were examined. Results: Less than one third of positive messages (31.7%) were shared at least once. In adjusted models, accounts that posted a greater number of messages were less likely to have any single message shared. Messages about military-related topics were 60% more likely to be shared (adjusted odds ratio [AOR] = 1.6, 95% CI [1.1, 2.1]) as well as messages containing achievement-related keywords (AOR = 1.6, 95% CI [1.3, 1.9]). Conversely, positive messages explicitly addressing eating/food, appearance, and sad affective states were less likely to be shared. Multiple other message characteristics influenced sharing. Limitations: Only messages on a single platform and over a focused period of time were analyzed. Conclusion: A knowledge of factors affecting dissemination of positive mental health messages may aid organizations and individuals seeking to promote such messages online.

Ability of crime, demographic and business data to forecast areas of increased violence.

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.