OBJECTIVE(S): To examine associations between Index of Concentration at the Extremes (ICE) measures (proxy for structural racism) for economic and Hispanic/Latino-White racial segregation and HIV outcomes among adults in the U.S. METHODS: Census tract-level HIV diagnoses, linkage to HIV medical care within 1 month of diagnosis (linkage), and viral suppression within 6 months of diagnosis (viral suppression) data for 2021 from the National HIV Surveillance System were used. Three ICE measures were obtained from the American Community Survey: ICEincome (income segregation), ICErace (Hispanic/Latino-White racial segregation), and ICEincome + race (Hispanic/Latino-White racialized economic segregation). Rate ratios (RRs) for HIV diagnosis and prevalence ratios (PRs) for linkage and viral suppression were used to examine differences in HIV outcomes across ICE quintiles with Quintile5 (Q5: most privileged) as reference group and adjusted by selected characteristics. RESULTS: Among the 32,529 adults, diagnosis rates were highest in Quintile1 (Q1: most deprived) for ICEincome (28.7) and ICEincome + race (28.4) and Q2 for ICErace (27.0). We also observed higher RRs in HIV diagnosis and lower PRs in linkage and viral suppression (except for ICErace for linkage) in Q1 compared to Q5. Higher RRs and lower PRs in ICE measures were observed among males (diagnosis), adults aged 18‒34 (diagnosis and linkage) and aged ≥ 45 (viral suppression), and among adults in the South (all 3 HIV outcomes). CONCLUSIONS: Barriers in access to care/treatment in more Hispanic/Latino-White racialized economic segregated communities perpetuate the disproportionate impact of HIV on the population. Removing barriers to HIV care/treatment created by systemic racism/segregation may improve HIV outcomes and reduce disparities.

We describe humoral immune responses in 105 ambulatory patients with laboratory-confirmed SARS-CoV-2 Omicron variant infection. In dried blood spot (DBS) collected within 5 days of illness onset and during convalescence, we measured binding antibody (bAb) against ancestral spike protein receptor binding domain (RBD) and nucleocapsid (N) protein using a commercial multiplex bead assay. Geometric mean bAb concentrations against RBD increased by a factor of 2.5 from 1258 to 3189 units/mL and by a factor of 47 against N protein from 5.5 to 259 units/mL between acute illness and convalescence; lower concentrations were associated with greater geometric mean ratios. Paired DBS specimens may be used to evaluate humoral response to SARS-CoV-2 infection.

BACKGROUND: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent's gender. OBJECTIVE: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide. METHODS: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model. RESULTS: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause. CONCLUSIONS: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

BACKGROUND: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. OBJECTIVE: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. METHODS: We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. RESULTS: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. CONCLUSIONS: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

Background: In 2022, suicide ranked as the 11th leading cause of death in the United States with 49,513 deaths. Provisional mortality data from 2022 indicate a 2.8% increase in the number of suicides compared to 2021. This paper examines overall suicide trends, sodium nitrite ingestion as an emerging suicide method, and the role that online forums play in sharing information about suicide methods (including sodium nitrite ingestion). Methods: Suicides were identified from CDC's National Vital Statistics System (2018-July 2023 provisional) multiple cause-of-death mortality files using International Classification of Diseases (ICD), Tenth Revision underlying cause-of-death codes U03, X60–X84, and Y87.0 and T code T50.6 (antidotes and chelating agents). Google search popularity metrics were captured from January 2019 to January 2023. Case reports of sodium nitrite related suicide and suicide attempts (through February 2024) were identified in the medical and forensic literature. Results: At least 768 suicides involving antidotes and chelating agents (including sodium nitrite) occurred between 2018 and July 2023, set in the context of 268,972 total suicides during that period. Overall, suicides involving antidotes and chelating agents (including sodium nitrite) represent <1% of all suicides, however, numbers are rising. Conclusions: Suicide methods are known to change over time. These changes can be influenced by, among other factors, online forums and means accessibility, such as internet purchase availability. CDC remains committed to prevention through comprehensive public health strategies that protect individuals, families, and communities. Practical Applications: States and community partners might consider leveraging physicians, emergency responders, and other appropriate crisis response groups to disseminate information on sodium nitrite self-poisoning and its antidote, methylene blue. Efforts should be part of a comprehensive public health approach to suicide prevention. © 2024

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

BACKGROUND: We assessed associations between binding antibody (bAb) concentration <5 days of symptom onset and testing positive for COVID-19 among patients in a test-negative study. METHODS: From October 2021─June 2022, study sites in seven states enrolled patients aged ≥6 months presenting with acute respiratory illness. Respiratory specimens were tested for SARS-CoV-2. In blood specimens, we measured concentrations of anti-SARS-CoV-2 antibodies against the ancestral strain spike protein receptor binding domain (RBD) and nucleocapsid (N) antigens in standardized binding antibody units (BAU/mL). Percent change in odds of COVID-19 by increasing anti-RBD bAb was estimated using logistic regression as (1-adjusted odds ratio of COVID-19)x100, adjusting for COVID-19 mRNA vaccine doses, age, site, and high-risk exposure. RESULTS: Out of 2,018 symptomatic patients, 662 (33%) tested positive for acute SARS-CoV-2 infection. Geometric mean RBD bAb were lower among COVID-19 cases than SARS-CoV-2 test-negative patients during both the Delta-predominant (112 vs. 498 BAU/mL) and Omicron-predominant (823 vs. 1,189 BAU/mL) periods. Acute phase ancestral spike RBD bAb associated with 50% lower odds of COVID-19 were 1,968 BAU/mL against Delta and 3,375 BAU/mL against Omicron; thresholds may differ in other laboratories. CONCLUSION: During acute illness, antibody concentrations against ancestral spike RBD were associated with protection against COVID-19.

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.

BACKGROUND: Highly pathogenic avian influenza A(H5) human infections are a global concern, with many A(H5) human cases detected in Vietnam, including a case in October 2022. Using avian influenza virus surveillance from March 2017-September 2022, we described the percent of pooled samples that were positive for avian influenza A, A(H5), A(H5N1), A(H5N6), and A(H5N8) viruses in live bird markets (LBMs) in Vietnam. METHODS: Monthly at each LBM, 30 poultry oropharyngeal swab specimens and five environmental samples were collected. Samples were pooled in groups of five and tested for influenza A, A(H5), A(H5N1), A(H5N6), and A(H5N8) viruses by real-time reverse-transcription polymerase chain reaction. Trends in the percent of pooled samples that were positive for avian influenza were summarized by LBM characteristics and time and compared with the number of passively detected avian influenza outbreaks using Spearman's rank correlation. RESULTS: A total of 25,774 pooled samples were collected through active surveillance at 167 LBMs in 24 provinces; 36.9% of pooled samples were positive for influenza A, 3.6% A(H5), 1.9% A(H5N1), 1.1% A(H5N6), and 0.2% A(H5N8). Influenza A(H5) viruses were identified January-December and at least once in 91.7% of sampled provinces. In 246 A(H5) outbreaks in poultry; 20.3% were influenza A(H5N1), 60.2% A(H5N6), and 19.5% A(H5N8); outbreaks did not correlate with active surveillance. CONCLUSIONS: In Vietnam, influenza A(H5) viruses were detected by active surveillance in LBMs year-round and in most provinces sampled. In addition to outbreak reporting, active surveillance for A(H5) viruses in settings with high potential for animal-to-human spillover can provide situational awareness.

Suicide, including firearm suicide, remains a substantial public health concern in the United States. During the previous 2 decades, overall suicide rates and firearm suicide rates have risen by approximately one third, approaching 50,000 overall suicides during 2022, including approximately 27,000 firearm suicides (1). Firearm suicides account for approximately one half of all suicides, and this proportion has been increasing (2,3). This analysis includes national firearm suicide data from 2019 through the end of 2022, categorized by race and ethnicity, presented both annually and by month (or quarterly) to track subannual changes.

The rate of firearm homicide in the United States rose sharply from 2019 through 2020, reaching a level not seen in more than 2 decades, with ongoing and widening racial and ethnic disparities (1). During 2020–2021, the rate increased again (2). This report provides provisional firearm homicide data for 2022, stratified by race and ethnicity, presented both annually and by month (or quarter) to document subannual changes.

BACKGROUND: Influenza burden varies across seasons, partly due to differences in circulating influenza virus types or subtypes. Using data from the US population-based surveillance system, Influenza Hospitalization Surveillance Network (FluSurv-NET), we aimed to assess the severity of influenza-associated outcomes in individuals hospitalised with laboratory-confirmed influenza virus infections during the 2010-11 to 2018-19 influenza seasons. METHODS: To evaluate the association between influenza virus type or subtype causing the infection (influenza A H3N2, A H1N1pdm09, and B viruses) and in-hospital severity outcomes (intensive care unit [ICU] admission, use of mechanical ventilation or extracorporeal membrane oxygenation [ECMO], and death), we used FluSurv-NET to capture data for laboratory-confirmed influenza-associated hospitalisations from the 2010-11 to 2018-19 influenza seasons for individuals of all ages living in select counties in 13 US states. All individuals had to have an influenza virus test within 14 days before or during their hospital stay and an admission date between Oct 1 and April 30 of an influenza season. Exclusion criteria were individuals who did not have a complete chart review; cases from sites that contributed data for three or fewer seasons; hospital-onset cases; cases with unidentified influenza type; cases of multiple influenza virus type or subtype co-infection; or individuals younger than 6 months and ineligible for the influenza vaccine. Logistic regression models adjusted for influenza season, influenza vaccination status, age, and FluSurv-NET site compared odds of in-hospital severity by virus type or subtype. When missing, influenza A subtypes were imputed using chained equations of known subtypes by season. FINDINGS: Data for 122 941 individuals hospitalised with influenza were captured in FluSurv-NET from the 2010-11 to 2018-19 seasons; after exclusions were applied, 107 941 individuals remained and underwent influenza A virus imputation when missing A subtype (43·4%). After imputation, data for 104 969 remained and were included in the final analytic sample. Averaging across imputed datasets, 57·7% (weighted percentage) had influenza A H3N2, 24·6% had influenza A H1N1pdm09, and 17·7% had influenza B virus infections; 16·7% required ICU admission, 6·5% received mechanical ventilation or ECMO, and 3·0% died (95% CIs had a range of less than 0·1% and are not displayed). Individuals with A H1N1pdm09 had higher odds of in-hospital severe outcomes than those with A H3N2: adjusted odds ratios (ORs) for A H1N1pdm09 versus A H3N2 were 1·42 (95% CI 1·32-1·52) for ICU admission; 1·79 (1·60-2·00) for mechanical ventilation or ECMO use; and 1·25 (1·07-1·46) for death. The adjusted ORs for individuals infected with influenza B versus influenza A H3N2 were 1·06 (95% CI 1·01-1·12) for ICU admission, 1·14 (1·05-1·24) for mechanical ventilation or ECMO use, and 1·18 (1·07-1·31) for death. INTERPRETATION: Despite a higher burden of hospitalisations with influenza A H3N2, we found an increased likelihood of in-hospital severe outcomes in individuals hospitalised with influenza A H1N1pdm09 or influenza B virus. Thus, it is important for individuals to receive an annual influenza vaccine and for health-care providers to provide early antiviral treatment for patients with suspected influenza who are at increased risk of severe outcomes, not only when there is high influenza A H3N2 virus circulation but also when influenza A H1N1pdm09 and influenza B viruses are circulating. FUNDING: The US Centers for Disease Control and Prevention.

BACKGROUND: Suicide mortality data are a critical source of information for understanding suicide-related trends in the United States. However, official suicide mortality data experience significant delays. The Google Symptom Search Dataset (SSD), a novel population-level data source derived from online search behavior, has not been evaluated for its utility in predicting suicide mortality trends. METHODS: We identified five mental health related variables (suicidal ideation, self-harm, depression, major depressive disorder, and pain) from the SSD. Daily search trends for these symptoms were utilized to estimate national and state suicide counts in 2020, the most recent year for which data was available, via a linear regression model. We compared the performance of this model to a baseline autoregressive integrated moving average (ARIMA) model and a model including all 422 symptoms (All Symptoms) in the SSD. RESULTS: Our Mental Health Model estimated the national number of suicide deaths with an error of -3.86 %, compared to an error of 7.17 % and 28.49 % for the ARIMA baseline and All Symptoms models. At the state level, 70 % (N = 35) of states had a prediction error of <10 % with the Mental Health Model, with accuracy generally favoring larger population states with higher number of suicide deaths. CONCLUSION: The Google SSD is a new real-time data source that can be used to make accurate predictions of suicide mortality monthly trends at the national level. Additional research is needed to optimize state level predictions for states with low suicide counts.

BACKGROUND: Assessing individual- and community-level factors may help to explain differences among Hispanic/Latino adults with diagnosed HIV not linked to care and without viral suppression in the United States. METHODS: We analyzed CDC's National HIV Surveillance System data among Hispanic/Latino persons aged ≥ 18 years with HIV diagnosed during 2021 in 47 states and the District of Columbia and linked cases via census tracts to the CDC/ATSDR's Social Vulnerability Index (SVI). Adjusted prevalence ratios and 95% confidence intervals for non-linkage to care and non-viral suppression were estimated using Poisson regression model. RESULTS: Among 5,056 Hispanic/Latino adults with HIV diagnosed in 2021, 51.5% were born in the United States, 17.3% in Mexico, 9.2% in Central America, 11.1% in South America, 1.8% in Puerto Rico, 6.8% in Cuba, and 2.4% in the Caribbean. Compared with U.S.-born Hispanic/Latino adults, those born in Mexico and South America had a lower prevalence of non-linkage to care. Hispanic/Latino adults born in Mexico, South America, and the Caribbean (excluding Puerto Rico and Cuba) had a lower prevalence of non-viral suppression, compared with those born in the United States. No significant differences were observed among SVI quartiles for either care outcome. CONCLUSION: This study aimed to challenge the narrow perspective on HIV care outcomes by examining the impact of birthplace and social vulnerability among Hispanic/Latino adults. To increase HIV care and prevention among Hispanic/Latino persons, research must evaluate health disparities within the group, and efforts are needed to better understand and tailor interventions within the diverse Hispanic/Latino population.

Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges like exposure to intimate partner violence and substance use in the home can have negative impacts on lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced ACEs. However, how social networks of those who experienced ACEs differ from those who did not is poorly understood. In the present study, we use Reddit and Twitter data to investigate and compare social networks among individuals with and without ACEs exposure. We first use a neural network classifier to identify the presence or absence of public ACEs disclosures in social media posts. We then analyze egocentric social networks comparing individuals with self-reported ACEs to those with no reported history. We found that, although individuals reporting ACEs had fewer total followers in online social networks, they had higher reciprocity in following behavior (i.e., mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. These results imply that individuals with ACEs may try to actively connect to others having similar prior traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections online for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.

During February 7─September 3, 2022, a total of 39 US states experienced outbreaks of highly pathogenic avian influenza A(H5N1) virus in birds from commercial poultry farms and backyard flocks. Among persons exposed to infected birds, highly pathogenic avian influenza A(H5) viral RNA was detected in 1 respiratory specimen from 1 person.

BACKGROUND: Knowledge of the specific dynamics of influenza introduction and spread in university settings is limited. METHODS: Persons with acute respiratory illness symptoms received influenza testing by molecular assay during October 6-November 23, 2022. Viral sequencing and phylogenetic analysis were conducted on nasal swab samples from case-patients. Case-control analysis of a voluntary survey of persons tested was used to identify factors associated with influenza; logistic regression was conducted to calculate odds ratios and 95% CIs. A subset of case-patients tested during the first month of the outbreak was interviewed to identify sources of introduction and early spread. RESULTS: Among 3268 persons tested, 788 (24.1%) tested positive for influenza; 744 (22.8%) were included in the survey analysis. All 380 sequenced specimens were influenza A (H3N2) virus clade 3C.2a1b.2a.2, suggesting rapid transmission. Influenza (OR [95% CI]) was associated with indoor congregate dining (1.43 [1.002-2.03]), attending large gatherings indoors (1.83 [1.26-2.66]) or outdoors (2.33 [1.64-3.31]), and varied by residence type (apartment with ≥1 roommate: 2.93 [1.21-7.11], residence hall room alone: 4.18 [1.31-13.31], or with roommate: 6.09 [2.46-15.06], or fraternity/sorority house: 15.13 [4.30-53.21], all compared with single-dwelling apartment). Odds of influenza were lower among persons who left campus for ≥1 day during the week before their influenza test (0.49 [0.32-0.75]). Almost all early cases reported attending large events. CONCLUSIONS: Congregate living and activity settings on university campuses can lead to rapid spread of influenza following introduction. Isolating following a positive influenza test or administering antiviral medications to exposed persons may help mitigate outbreaks.

BACKGROUND: Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges such as exposure to intimate partner violence and substance use in the home, can have negative impacts on the lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced them. However, how the social networks of those who experienced ACEs differ from the social networks of those who did not is poorly understood. OBJECTIVE: In this study, we used Reddit and Twitter data to investigate and compare social networks between individuals with and without ACE exposure. METHODS: We first used a neural network classifier to identify the presence or absence of public ACE disclosures in social media posts. We then analyzed egocentric social networks comparing individuals with self-reported ACEs with those with no reported history. RESULTS: We found that, although individuals reporting ACEs had fewer total followers in web-based social networks, they had higher reciprocity in following behavior (ie, mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. CONCLUSIONS: These results imply that individuals with ACEs may try to actively connect with others who have similar previous traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections on the web for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs.

Introduction: Despite a government-subsidized vaccination program, healthcare personnel (HCP) influenza vaccination uptake remains low in Peru. Using three years of cross-sectional surveys and an additional five years of prior vaccination history of HCP in Peru, we explored HCP knowledge, attitudes, and practices (KAP) of influenza illness and its impact on vaccination frequency. Methods: In 2016, the Estudio Vacuna de Influenza Peru (VIP) cohort was initiated in Lima, Peru, which collected information about HCP KAP and influenza vaccination history from 20112018. HCP were classified by their 8-year influenza vaccination history as never (0 years), infrequently (14 years), or frequently (58 years) vaccinated. Logistic regression models were used to describe KAP associated with frequent compared to infrequent influenza vaccination, adjusted for each HCP's healthcare workplace, age, sex, preexisting medical conditions, occupation, and length of time providing direct patient care. Results: From 20162018, 5131 HCP were recruited and 3120 fully enrolled in VIP; 2782 consistently reported influenza vaccination status and became our analytic sample. From 20112018, 14.3% of HCP never, 61.4% infrequently, and 24.4% frequently received influenza vaccines. Compared to HCP who were infrequently vaccinated, frequently vaccinated HCP were more likely to believe they were susceptible to influenza (adjusted odds ratio [aOR]:1.49, 95% confidence interval [CI]:1.221.82), perceived vaccination to be effective (aOR:1.92, 95%CI:1.592.32), were knowledgeable about influenza and vaccination (aOR:1.37, 95%CI:1.061.77), and believed vaccination had emotional benefits like reduced regret or anger if they became ill with influenza (aOR:1.96, 95%CI:1.602.42). HCP who reported vaccination barriers like not having time or a convenient place to receive vaccines had reduced odds of frequent vaccination (aOR:0.74, 95%CI:0.610.89) compared to those without reported barriers. Conclusion: Few HCP frequently received influenza vaccines during an eight-year period. To increase HCP influenza vaccination in middle-income settings like Peru, campaigns could strengthen influenza risk perception, vaccine knowledge, and accessibility. 2023

Introduction: Despite a government-subsidized vaccination program, healthcare personnel (HCP) influenza vaccination uptake remains low in Peru. Using three years of cross-sectional surveys and an additional five years of prior vaccination history of HCP in Peru, we explored HCP knowledge, attitudes, and practices (KAP) of influenza illness and its impact on vaccination frequency. Methods: In 2016, the Estudio Vacuna de Influenza Peru (VIP) cohort was initiated in Lima, Peru, which collected information about HCP KAP and influenza vaccination history from 2011─2018. HCP were classified by their 8-year influenza vaccination history as never (0 years), infrequently (1─4 years), or frequently (5─8 years) vaccinated. Logistic regression models were used to describe KAP associated with frequent compared to infrequent influenza vaccination, adjusted for each HCP's healthcare workplace, age, sex, preexisting medical conditions, occupation, and length of time providing direct patient care. Results: From 2016─2018, 5131 HCP were recruited and 3120 fully enrolled in VIP; 2782 consistently reported influenza vaccination status and became our analytic sample. From 2011─2018, 14.3% of HCP never, 61.4% infrequently, and 24.4% frequently received influenza vaccines. Compared to HCP who were infrequently vaccinated, frequently vaccinated HCP were more likely to believe they were susceptible to influenza (adjusted odds ratio [aOR]:1.49, 95% confidence interval [CI]:1.22─1.82), perceived vaccination to be effective (aOR:1.92, 95%CI:1.59─2.32), were knowledgeable about influenza and vaccination (aOR:1.37, 95%CI:1.06─1.77), and believed vaccination had emotional benefits like reduced regret or anger if they became ill with influenza (aOR:1.96, 95%CI:1.60─2.42). HCP who reported vaccination barriers like not having time or a convenient place to receive vaccines had reduced odds of frequent vaccination (aOR:0.74, 95%CI:0.61─0.89) compared to those without reported barriers. Conclusion: Few HCP frequently received influenza vaccines during an eight-year period. To increase HCP influenza vaccination in middle-income settings like Peru, campaigns could strengthen influenza risk perception, vaccine knowledge, and accessibility. © 2023

The article is previously published with incorrect and missing information. The updates are as follows: | | The last sentence in the third paragraph of ‘Building hospital capacity for data collection’ in ‘Results’ section has been updated as ‘A one-way ANOVA revealed a significant difference between April 2015 and April 2016 triage times, F(1,2734)=5.33, p=0.02. Triage times were on average 16.2 s longer in April 2016 compared with April 2015. No post-hoc analyses were done to control for other, non-CMST-related changes that occurred during the triage process (eg, additional triage screen) from April 2015 to April 2016.’ | Below statement has been added in the sixth paragraph of the ‘Discussion’ section after ‘Nurse participation in the satisfaction … a different US hospital.’ | The statistically significant increase in triage time of 16.2 s, which is unlikely to be clinically significant, may reflect other non-CMST-related triage process changes - such as addition of another triage screen - that were not accounted for in the analyses.

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.

INTRODUCTION: Stigma associated with substance use and addiction is a major barrier to overdose prevention. Although stigma reduction is a key goal of federal strategies to prevent overdose, there is limited data to assess progress made in reducing use of stigmatizing language about addiction. METHODS: Using language guidelines published by the federal National Institute on Drug Abuse (NIDA), we examined trends in use of stigmatizing terms about addiction across four popular public communication modalities: news articles, blogs, Twitter, and Reddit. We calculate percent changes in the rates of articles/posts using stigmatizing terms over a five-year period (2017-2021) by fitting a linear trendline and assess statistically significant trends using the Mann-Kendall test. RESULTS: The rate of articles containing stigmatizing language decreased over the past five years for news articles (-68.2 %, p < 0.001) and blogs (-33.6 %, p < 0.001). Among social media platforms, the rate of posts using stigmatizing language increased (Twitter [43.5 %, p = 0.01]) or remained stable (Reddit [3.1 %, p = 0.29]). In absolute terms, news articles had the highest rate of articles containing stigmatizing terms over the five-year period (324.9 articles per million) compared to 132.3, 18.3, and 138.6 posts per million for blogs, Twitter, and Reddit, respectively. CONCLUSIONS: Use of stigmatizing language about addiction appears to have decreased across more traditional, longer-format communication modalities such as news articles. Additional work is needed to reduce use of stigmatizing language on social media.

Participation in communities is essential to individual mental and physical health and can yield further benefits for members. With a growing amount of time spent participating in virtual communities, it's increasingly important that we understand how the community experience manifests in and varies across these online spaces. In this paper, we investigate Sense of Virtual Community (SOVC) in the context of live-streaming communities. Through a survey of 1,944 Twitch viewers, we identify that community experiences on Twitch vary along two primary dimensions: belonging, a feeling of membership and support within the group, and cohesion, a feeling that the group is a well-run collective with standards for behavior. Leveraging the Social-Ecological Model, we map behavioral trace data from usage logs to various levels of the social ecology surrounding an individual user's participation within a community, in order to identify which of these can be associated with lower or higher SOVC. We find that features describing activity at the individual and community levels, but not features describing the community member's dyadic relationships, aid in predicting the SOVC that community members feel within channels. We consider implications for the design of live-streaming communities and for fostering the well-being of their members, and we consider theoretical implications for the study of SOVC in modern, interactive online contexts, particularly those fostering large-scale or pseudonymized interactions. We also explore how the Social-Ecological Model can be leveraged in other contexts relevant to Computer-Supported Cooperative Work (CSCW), with implications for future work. © 2022 ACM.

Influenza seasons typically begin in October and peak between December and February (1); however, the 2022-23 influenza season in Tennessee began in late September and was characterized by high pediatric hospitalization rates during November. This report describes a field investigation conducted in Tennessee during November 2022, following reports of increasing influenza hospitalizations. Data from surveillance networks, patient surveys, and whole genome sequencing of influenza virus specimens were analyzed to assess influenza activity and secondary illness risk. Influenza activity increased earlier than usual among all age groups, and rates of influenza-associated hospitalization among children were high in November, reaching 12.6 per 100,000 in children aged <5 years, comparable to peak levels typically seen in high-severity seasons. Circulating influenza viruses were genetically similar to vaccine components. Among persons who received testing for influenza at outpatient clinics, children were twice as likely to receive a positive influenza test result as were adults. Among household contacts exposed to someone with influenza, children were more than twice as likely to become ill compared with adults. As the influenza season continues, it is important for all persons, especially those at higher risk for severe disease, to protect themselves from influenza. To prevent influenza and severe influenza complications, all persons aged ≥6 months should get vaccinated, avoid contact with ill persons, and take influenza antivirals if recommended and prescribed.

Illicit or 'designer' benzodiazepines are a growing contributor to overdose deaths. We employed natural language processing (NLP) to study benzodiazepine mentions over 10 years on 270 online drug forums (subreddits) on Reddit. Using NLP, we automatically detected mentions of illicit and prescription benzodiazepines, including their misspellings and non-standard names, grouping relative mentions by quarter. On a collection of 17 861 755 posts between 2012 and 2021, we searched for 26 benzodiazepines (8 prescription; 18 illicit), detecting 173 275 mentions. The rate of posts about both prescription and illicit benzodiazepines increased consistently with increases in deaths involving both drug classes, illustrating the utility of surveillance via Reddit.

OBJECTIVE: The aim of this study was to assess associations of various content areas of Twitter posts with help-seeking from the US National Suicide Prevention Lifeline (Lifeline) and with suicides. METHODS: We retrieved 7,150,610 suicide-related tweets geolocated to the United States and posted between 1 January 2016 and 31 December 2018. Using a specially devised machine-learning approach, we categorized posts into content about prevention, suicide awareness, personal suicidal ideation without coping, personal coping and recovery, suicide cases and other. We then applied seasonal autoregressive integrated moving average analyses to assess associations of tweet categories with daily calls to the US National Suicide Prevention Lifeline (Lifeline) and suicides on the same day. We hypothesized that coping-related and prevention-related tweets are associated with greater help-seeking and potentially fewer suicides. RESULTS: The percentage of posts per category was 15.4% (standard deviation: 7.6%) for awareness, 13.8% (standard deviation: 9.4%) for prevention, 12.3% (standard deviation: 9.1%) for suicide cases, 2.4% (standard deviation: 2.1%) for suicidal ideation without coping and 0.8% (standard deviation: 1.7%) for coping posts. Tweets about prevention were positively associated with Lifeline calls (B=1.94, SE=0.73, p=0.008) and negatively associated with suicides (B=-0.11, standard error=0.05, p=0.038). Total number of tweets were negatively associated with calls (B=-0.01, standard error =0.0003, p=0.007) and positively associated with suicide, (B=6.410(-5), standard error =2.610(-5), p=0.015). CONCLUSION: This is the first large-scale study to suggest that daily volume of specific suicide-prevention-related social media content on Twitter corresponds to higher daily levels of help-seeking behaviour and lower daily number of suicide deaths. PREREGISTRATION: As Predicted, #66922, 26 May 2021.

BACKGROUND: Households are common places for spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated factors associated with household transmission and acquisition of SARS-CoV-2. METHODS: Households with children age <18 years were enrolled into prospective, longitudinal cohorts and followed from August 2020 to August 2021 in Utah, September 2020 to August 2021 in New York City, and November 2020 to October 2021 in Maryland. Participants self-collected nasal swabs weekly and with onset of acute illness. Swabs were tested for SARS-CoV-2 using reverse transcription polymerase chain reaction. We assessed factors associated with SARS-CoV-2 acquisition using a multilevel logistic regression adjusted for household size and clustering and SARS-CoV-2 transmission using a logistic regression adjusted for household size. RESULTS: Among 2053 people (513 households) enrolled, 180 people (8.8%; in 76 households) tested positive for SARS-CoV-2. Compared with children age <12 years, the odds of acquiring infection were lower for adults age ≥18 years (adjusted odds ratio [aOR], 0.34; 95% CI, 0.14-0.87); however, this may reflect vaccination status, which protected against SARS-CoV-2 acquisition (aOR, 0.17; 95% CI, 0.03-0.91). The odds of onward transmission were similar between symptomatic and asymptomatic primary cases (aOR, 1.00; 95% CI, 0.35-2.93) and did not differ by age (12-17 years vs <12 years: aOR, 1.08; 95% CI, 0.20-5.62; ≥18 years vs <12 years: aOR, 1.70; 95% CI, 0.52-5.83). CONCLUSIONS: Adults had lower odds of acquiring SARS-CoV-2 compared with children, but this association might be influenced by coronavirus disease 2019 (COVID-19) vaccination, which was primarily available for adults and protective against infection. In contrast, all ages, regardless of symptoms and COVID-19 vaccination, had similar odds of transmitting SARS-CoV-2. Our findings underscore the importance of SARS-CoV-2 mitigation measures for persons of all ages.

IMPORTANCE: Opioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES: To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, signals from 5 overdose-related, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES: Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES: Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTS: Statistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and -1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCE: Results of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.