BACKGROUND: SMS text messaging- and internet-based self-reporting systems can supplement existing vaccine safety surveillance systems, but real-world participation patterns have not been assessed at scale. OBJECTIVE: This study aimed to describe the participation rates of a new SMS text messaging- and internet-based self-reporting system called the Kaiser Permanente Side Effect Monitor (KPSEM) within a large integrated health care system. METHODS: We conducted a prospective cohort study of Kaiser Permanente Southern California (KPSC) patients receiving a COVID-19 vaccination from April 23, 2021, to July 31, 2023. Patients received invitations through flyers, SMS text messages, emails, or patient health care portals. After consenting, patients received regular surveys to assess adverse events up to 5 weeks after each dose. Linkage with medical records provided demographic and clinical data. In this study, we describe KPSEM participation rates, defined as providing consent and completing at least 1 survey within 35 days of COVID-19 vaccination. RESULTS: Approximately, 8% (164,636/2,091,975) of all vaccinated patients provided consent and completed at least 1 survey within 35 days. The lowest participation rates were observed for parents of children aged 12-17 years (1349/152,928, 0.9% participation rate), and the highest participation was observed among older adults aged 61-70 years (39,844/329,487, 12.1%). Persons of non-Hispanic White race were more likely to participate compared with other races and ethnicities (13.1% vs 3.9%-7.5%, respectively; P<.001). In addition, patients residing in areas with a higher neighborhood deprivation index were less likely to participate (5.1%, 16,503/323,122 vs 10.8%, 38,084/352,939 in the highest vs lowest deprivation quintiles, respectively; P<.001). Invitations through the individual's Kaiser Permanente health care portal account and by SMS text message were associated with the highest participation rate (19.2%, 70,248/366,377 and 10.5%, 96,169/914,793, respectively), followed by email (19,464/396,912, 4.9%) and then QR codes on flyers (25,882/2,091,975, 1.2%). SMS text messaging-based surveys demonstrated the highest sustained daily response rates compared with internet-based surveys. CONCLUSIONS: This real-world prospective study demonstrated that a novel digital vaccine safety self-reporting system implemented through an integrated health care system can achieve high participation rates. Linkage with participants' electronic health records is another unique benefit of this surveillance system. We also identified lower participation among selected vulnerable populations, which may have implications when interpreting data collected from similar digital systems.

Invasive meningococcal disease (IMD), caused by infection with the bacterium Neisseria meningitidis, usually manifests as meningitis or septicemia and can be severe and life-threatening (1). Six serogroups (A, B, C, W, X, and Y) account for most cases (2). N. meningitidis is transmitted person-to-person via respiratory droplets and oropharyngeal secretions. Asymptomatic persons can carry N. meningitidis and transmit the bacteria to others, potentially causing illness among susceptible persons. Outbreaks can occur in conjunction with large gatherings (3,4). Vaccines are available to prevent meningococcal disease. Antibiotic prophylaxis for close contacts of infected persons is critical to preventing secondary cases (2).

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Our aim was to investigate the impact of shiftwork on changes in central retinal arteriolar equivalent (CRAE), a measure of arteriolar width, and central retinal venular equivalent (CRVE), a measure of venular width, over five years. The participants were 117 officers (72.7% men) examined at the first (2011-2014) and second (2015-2019) follow-up examinations in the Buffalo Cardio-Metabolic Occupational Police Stress study. Shiftwork data were obtained from the City of Buffalo, NY payroll records. Retinal diameters were measured using a standardized protocol. ANCOVA was used to compare mean change in CRAE and CRVE between the two examinations across shiftwork categories. Among men only, those who worked ≥70% hours on day shifts had a larger decrease in mean CRAE (-7.13 µm ± 2.51) compared to those who worked <70% day (-0.08 ± 0.96; p = 0.011). Among patrol officers, those who worked ≥70% day had a larger decrease in CRAE compared to those who worked <70% day (p = 0.015). Also, officers who worked ≥70% day had an increase in mean CRVE (µm) (4.56 ± 2.56) compared to those who worked <70% (-2.32 ± 1.32; p = 0.027). Over the five-year period, we observed adverse changes in arteriolar and venular diameters among officers who worked ≥70% on day shifts. The results should be interpreted with caution due to the small sample sizes.

PURPOSE –: This study is a mortality assessment on police officers (68-years, 1950-2018) and includes all causes of death. DESIGN/METHODOLOGY/APPROACH –: The authors investigated 1,853 police deaths (1950-2018) using sources of mortality that included the National Death Index, NY State, and available records from the Buffalo NY police department. Standardized Mortality Ratios were calculated. Death codes were obtained from 8th and 9th International Classification of Disease revisions in accordance with the year of death. FINDINGS –: Compared to the US general population, white male police officers from 1950-2018 had elevated mortality rates for some causes of death, including diseases of the circulatory system, malignant neoplasms, cirrhosis of the liver, and mental disorders. Black and female officers had lower mortality rates for all causes of death compared to the general population. RESEARCH LIMITATIONS/IMPLICATIONS –: The findings of elevated risk for chronic disease among police need to be studied in relation to stress, lifestyle, and exposure to chemical and physical agents. There is a special need to further study officers from minority populations as larger samples become available. PRACTICAL IMPLICATIONS –: The results of this study will provide police and occupational health practitioners with objective evidence to determine the health impact of work on law enforcement officers. ORIGINALITY/VALUE –: This study is longest running mortality assessment on police officers ever conducted (1950-2018) and includes white, black, and female officers.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Author summary  • Human immunodeficiency virus (HIV) drug resistance has implications for antiretroviral treatment strategies and for containing the HIV pandemic because the development of HIV drug resistance leads to the requirement for antiretroviral drugs that may be less effective, less well-tolerated, and more expensive than those used in first-line regimens.  • HIV drug resistance studies are designed to determine which HIV mutations are selected by antiretroviral drugs and, in turn, how these mutations affect antiretroviral drug susceptibility and response to future antiretroviral treatment regimens.  • Such studies collectively form a vital knowledge base essential for monitoring global HIV drug resistance trends, interpreting HIV genotypic tests, and updating HIV treatment guidelines.  • Although HIV drug resistance data are collected in many studies, such data are often not publicly shared, prompting the need to recommend best practices to encourage and standardize HIV drug resistance data sharing.  • In contrast to other viruses, sharing HIV sequences from phylogenetic studies of transmission dynamics requires additional precautions as HIV transmission is criminalized in many countries and regions.  • Our recommendations are designed to ensure that the data that contribute to HIV drug resistance knowledge will be available without undue hardship to those publishing HIV drug resistance studies and without risk to people living with HIV.

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information&amp; Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. &amp; Charlene Zalesky, and Claudia &amp; Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill &amp; Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, &amp; Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has spread globally, resulting in >300,000 reported cases worldwide as of March 21st, 2020. Here we investigate the genetic diversity and genomic epidemiology of SARS-CoV-2 in Northern California using samples from returning travelers, cruise ship passengers, and cases of community transmission with unclear infection sources. Virus genomes were sampled from 29 patients diagnosed with COVID-19 infection from Feb 3rd through Mar 15th. Phylogenetic analyses revealed at least 8 different SARS-CoV-2 lineages, suggesting multiple independent introductions of the virus into the state. Virus genomes from passengers on two consecutive excursions of the Grand Princess cruise ship clustered with those from an established epidemic in Washington State, including the WA1 genome representing the first reported case in the United States on January 19th. We also detected evidence for presumptive transmission of SARS-CoV-2 lineages from one community to another. These findings suggest that cryptic transmission of SARS-CoV-2 in Northern California to date is characterized by multiple transmission chains that originate via distinct introductions from international and interstate travel, rather than widespread community transmission of a single predominant lineage. Rapid testing and contact tracing, social distancing, and travel restrictions are measures that will help to slow SARS-CoV-2 spread in California and other regions of the USA.

OBJECTIVE: To compare health-related quality of life (HRQOL) and pelvic pain levels over time in patients with interstitial cystitis/bladder pain syndrome (IC/BPS) and those with other pelvic pain conditions (OPPC) including chronic prostatitis, dyspareunia, vaginismus, vulvodynia, and vulvar vestibulitis. METHODS: We prospectively enrolled male and female patients from any Veterans Health Administration (VHA) center in the US. They completed the Genitourinary Pain Index (GUPI) quantifying urologic HRQOL and the 12-Item Short Form Survey version 2 (SF-12) quantifying general HRQOL at enrollment and 1 year later. Participants were classified by ICD diagnosis codes and confirmed by chart review to be IC/BPS or OPPC (308 and 85 patients respectively). RESULTS: At baseline and follow-up, IC/BPS patients, on average, had worse urologic and general HRQOL than OPPC patients. IC/BPS patients demonstrated improvement in urologic HRQOL measures over the study but demonstrated no significant change in any general HRQOL measure suggesting a condition-specific impact. Patients with OPPC demonstrated similar improvements in urologic HRQOL but had deteriorating mental health and general HRQOL at follow-up suggesting a wider general HRQOL impact for these diseases. CONCLUSIONS: We found that patients with IC/BPS had worse urologic HRQOL compared to other pelvic conditions. Despite this, IC/BPS showed stable general HRQOL over time, suggesting a more condition-specific impact on HRQOL. OPPC patients showed deteriorating general HRQOL, suggesting more widespread pain symptoms in these conditions.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread throughout the world. On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic. Genome sequencing of SARS-CoV-2 strains allows for the reconstruction of transmission history connecting these infections. Here, we analyze 346 SARS-CoV-2 genomes from samples collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We found that the large majority of SARS-CoV-2 infections sampled during this time frame appeared to have derived from a single introduction event into the state in late January or early February 2020 and subsequent local spread, strongly suggesting cryptic spread of COVID-19 during the months of January and February 2020, before active community surveillance was implemented. We estimate a common ancestor of this outbreak clade as occurring between 18 January and 9 February 2020. From genomic data, we estimate an exponential doubling between 2.4 and 5.1 days. These results highlight the need for large-scale community surveillance for SARS-CoV-2 introductions and spread and the power of pathogen genomics to inform epidemiological understanding.

Few studies have evaluated gene-environment interaction in the risk of autism spectrum disorder (ASD). | | • | Single nucleotide polymorphism (SNPs) in relevant genes (e.g. immune, inflammatory, serotonin) were analyzed. | | • | Two experienced industrial hygienists estimated parental occupational exposure to solvents. | | • | Additive and multiplicative gene-environment interactions between SNPs and parental solventexposure were evaluated. | | • | SNP x solvent interaction is associated with higher rates of ASD compared to the SNP alone or solvent exposure alone.

The COVID-19 pandemic introduced a significant and unprecedented exacerbation of community mental health challenges. We compared the prevalence of mental health treatment (MHT) before and during the COVID-19 pandemic among US workers. Self-reported MHT data (N = 30,680) were obtained from the Sample Adult data of the National Health Interview Survey (2019 and 2020). MHT was defined as having taken prescription medications for mental health issues or receiving counseling from a mental health professional in the past 12 months. We calculated age-adjusted prevalence estimates and employed t-tests to compare MHT in 2019 and 2020 using SAS-callable SUDAAN 11.0. The prevalence of MHT significantly increased from 16.3% in 2019 to 17.6% in 2020 (difference = 1.3, p = 0.030). The prevalence of taking prescription medications for mental health issues significantly increased in 2020 compared to 2019 (12.5% to 13.6%, difference = 1.1, p = 0.037). The prevalence of receiving counseling significantly increased but only among those who worked 30-49 h/week, difference = 1.2, p = 0.022. US workers, especially those with typical work hours, appeared to experience higher mental distress during the first year of the pandemic compared to the year prior to the pandemic period. These findings highlight the need for targeted interventions to address mental health issues in these workers.

This study seeks to find the correlation between case fatality rates (CFRs) and third-dose vaccination coverage in 244 counties (si/gun/gu) of South Korea during the omicron variant wave. Multivariate regression analyses report that the higher third-dose vaccination rates were correlated with lower regional CFRs, when controlling for age structure. If the third-dose vaccination rate of a county is higher by 10%, it would have a CFR lower by 0.05% (95% confidence interval, 0.03-0.08%). As the number of cumulative confirmed cases in South Korea was 16,353,495 as of April 20, 2022, a lower CFR by 0.03-0.08% is equivalent to 4,394-12,448 lives (8.6-24.4 per 100,000) spared. County-specific characteristics, such as age structure, intensive care unit availability, and the level of non-pharmaceutical interventions may also affect the extent of this correlation. The conclusion implicates the potential role of coronavirus disease 2019 vaccines in reducing the pressure on the regional healthcare capacity.

Previous research has established the role of resistance training (RT) on muscle function in adolescents, but a lack of evidence to optimize RT for enhancing muscle quality (MQ) exists. This study examined whether RT frequency is associated with MQ in a nationally representative adolescent cohort. A total of 605 adolescents (12-15 year) in NHANES were stratified based on RT frequency. MQ was calculated as combined handgrip strength divided by arm lean mass (via dual-energy X-ray absorptiometry). Analysis of covariance was adjusted for sex, race/ethnicity, and arm fat percentage; p &lt; 0.05 was considered significant. RT frequency was associated with MQ for 2-7 day/week but not 1 day/week. When no RT was compared to 1-2 and 3-7 day/week, associations were present for 3-7 day/week but not 1-2 day/week. When comparing no RT to 1-4 and 5-7 day/week, associations existed for 5-7 day/week but not 1-4 day/week. Next, no RT was compared to 1, 2-3, and 4-7 day/week; associations were found for 4-7 day/week, while 2-3 day/week had a borderline association (p = 0.06); there were no associations for 1 day/week. Finally, no RT was compared to 1, 2, 3, 4, and 5-7 day/week; associations were present for all except 1 and 3 day/week. These prospective data suggest a minimum RT frequency of 2 day/week is associated with MQ in adolescents as indicated by the lack of differences in MQ between 1 day/week RT versus no RT.

OBJECTIVES: We examined associations of the metabolic syndrome severity score (MSSS) and the metabolic syndrome (MetSyn) components with central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE). METHODS: Participants in this cross-sectional study were 253 officers from the Buffalo Cardio-Metabolic Occupational Police Stress study (2011-2014). The MSSS is a z-score that represents the severity of MetSyn and was estimated using a sex-race-specific equation and the five MetSyn components. Associations of MSSS and the MetSyn with CRAE/CRVE were obtained using linear regression models or Analysis of Covariance. RESULTS: For every 1-standard deviation of MSSS, CRAE decreased by 2.3 m (SE = 1.2, p = 0.0262) and CRVE increased by 3.4 m (SE = 1.6, p = 0.0308) after adjusting for confounders. CONCLUSIONS: Officers with higher MSSS had narrower (i.e., worse) arteriolar diameters and wider (i.e., worse) venular diameters.

SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

IMPORTANCE: While diabetes prevalence among US adults has increased in recent decades, few studies document trends in diabetes-related eye disease. OBJECTIVE: To examine 10-year trends (2009-2018) in annual prevalence of Medicare beneficiaries with diabetes with a diagnosis of diabetic macular edema (DME) or vision-threatening diabetic retinopathy (VTDR) and trends in treatment. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study using Centers for Medicare & Medicaid Services research identifiable files, data for patients 65 years and older were analyzed from claims. Beneficiaries were continuously enrolled in Medicare Part B fee-for-service (FFS) insurance for the calendar year and had a diagnosis of diabetes on 1 or more inpatient claims or 2 or more outpatient claims during the calendar year or a 1-year look-back period. MAIN OUTCOMES AND MEASURES: Using diagnosis and procedure codes, annual prevalence was determined for beneficiaries with 1 or more claims for (1) any DME, (2) either DME or VTDR, and (3) anti-vascular endothelial growth factor (VEGF) injections, laser photocoagulation, or vitrectomy, stratified by any DME, VTDR with DME, and VTDR without DME. Racial and ethnic disparities in diagnosis and treatment are presented for 2018. RESULTS: In 2018, 6 960 823 beneficiaries (27.4%) had diabetes; half were aged 65 to 74 years (49.7%), half (52.7%) were women, and 75.7% were non-Hispanic White. From 2009 to 2018, there was an increase in the annual prevalence of beneficiaries with diabetes who had 1 or more claims for any DME (1.0% to 3.3%) and DME/VTDR (2.8% to 4.3%). Annual prevalence of anti-VEGF increased, particularly among patients with any DME (15.7% to 35.2%) or VTDR with DME (20.2% to 47.6%). Annual prevalence of laser photocoagulation decreased among those with any DME (45.5% to 12.5%), VTDR with DME (54.0% to 20.3%), and VTDR without DME (22.5% to 5.8%). Among all 3 groups, prevalence of vitrectomy in 2018 was less than half that in 2009. Prevalence of any DME and DME/VTDR was highest among Hispanic beneficiaries (5.0% and 7.0%, respectively) and Black beneficiaries (4.5% and 6.2%, respectively) and lowest among non-Hispanic White beneficiaries (3.0% and 3.8%, respectively). Among those with DME/VTDR, anti-VEGF was most prevalent among non-Hispanic White beneficiaries (30.3%). CONCLUSIONS AND RELEVANCE: From 2009 to 2018, prevalence of DME or VTDR increased among Medicare Part B FFS beneficiaries alongside an increase in anti-VEGF treatment and a decline in laser photocoagulation and vitrectomy.

BACKGROUND: Adverse Childhood Experiences (ACEs) are associated with numerous adverse health outcomes in adulthood. Our objective was to investigate associations between ACEs and sleep measures among 206 police officers from the Buffalo Cardio-Metabolic Occupational Police Stress study. METHODS: ACEs (independent variable) was assessed using the ACE questionnaire. Sleep measures were assessed using the Pittsburgh Sleep Quality Index and actigraphy. ANOVA/ANCOVA were used to investigate associations between ACEs and sleep measures. RESULTS: The mean self-reported sleep duration was significantly lower among participants who reported ≥1 ACEs compared to those who reported no ACEs (6.0 ± 0.11 vs. 6.4 ± 0.14 h; multivariate-adjusted p = 0.035). Sleep efficiency was significantly lower among participants who reported ≥1 ACEs compared to those who reported none (mean = 88.7%, 95% CI = 87.7-89.6 vs. 90.2%, 89.2-91.2; unadjusted p = 0.031) but was slightly attenuated and lost statistical significance after multivariate-adjustment (88.8%, 87.8-89.7 vs. 90.1%, 88.9-91.1; p = 0.094). Compared to participants who reported no ACEs, those who reported ≥1 ACEs had a higher mean activity index score (36.9 ± 0.96 vs. 31.2 ± 1.25; multivariate-adjusted p = 0.001); a higher mean wake after sleep onset (WASO) (44.3 ± 2.24 vs. 35.3 ± 2.92 min; multivariate-adjusted p = 0.019); and a higher sleep fragmentation index (3.8 ± 1.65 vs. 3.3 ± 1.20 unadjusted p = 0.044 and 3.8 ± 0.13 vs. 3.3 ± 0.18; multivariate-adjusted p = 0.062). Among men (but not women) who reported ≥1 ACEs, self-reported sleep duration was significantly lower (5.9 ± 0.13 vs. 6.4 ± 0.15 h; multivariate-adjusted p = 0.025) and activity index was significantly higher (39.1 ± 1.3 vs. 33.2 ± 1.51; multivariate-adjusted p = 0.004) compared to those who reported no ACEs. CONCLUSION: Exposure to ≥1 ACE was associated with worse sleep measures.

OBJECTIVE: Environmental exposures exacerbate age-related pathologies, such as cardiovascular and neurodegenerative diseases. Nanoparticulates, and specifically carbon nanomaterials, are a fast-growing contributor to the category of inhalable pollutants, whose risks to health are only now being unraveled. The current study assessed the exacerbating effect of age on multiwalled-carbon nanotube (MWCNT) exposure in young and old C57BL/6 and ApoE(-/-) mice. MATERIALS AND METHODS: Female C57BL/6 and apolipoprotein E-deficient (ApoE(-/-)) mice, aged 8weeks and 15months, were exposed to 0 or 40g MWCNT via oropharyngeal aspiration. Pulmonary inflammation, inflammatory bioactivity of serum, and neurometabolic changes were assessed at 24h post-exposure. RESULTS: Pulmonary neutrophil infiltration was induced by MWCNT in bronchoalveolar lavage fluid in both C57BL/6 and ApoE(-/-). Macrophage counts decreased with MWCNT exposure in ApoE(-/-) mice but were unaffected by exposure in C57BL/6 mice. Older mice appeared to have greater MWCNT-induced total protein in lavage fluid. BALF cytokines and chemokines were elevated with MWCNT exposure, but CCL2, CXCL1, and CXCL10 showed reduced responses to MWCNT in older mice. However, no significant serum inflammatory bioactivity was detected. Cerebellar metabolic changes in response to MWCNT were modest, but age and strain significantly influenced metabolite profiles assessed. ApoE(-/-) mice and older mice exhibited less robust metabolite changes in response to exposure, suggesting a reduced health reserve. CONCLUSIONS: Age influences the pulmonary and neurological responses to short-term MWCNT exposure. However, with only the model of moderate aging (15months) in this study, the responses appeared modest compared to inhaled toxicant impacts in more advanced aging models.

OBJECTIVE: This study aimed to evaluate trends in the use of obesogenic medications among adults. METHODS: Cross-sectional data on adults aged ≥20 years are from the 1999 to 2018 National Health and Nutrition Examination Survey (n = 52,340). Obesogenic medications were defined according to the 2015 Endocrine Society guidelines on the pharmacological management of obesity. Weight status was categorized according to BMI. Trends in prior 30-day use were evaluated. RESULTS: In NHANES 2017-2018, 20.3% of US adults used an obesogenic medication. Beta-blockers (9.8%) and antidiabetics (5.7%) were the most common; antipsychotics (1.0%) were the least common. Most common indications were disorders of glucose metabolism, hypertension, neuralgia or neuritis, heart disease, and musculoskeletal pain and/or inflammation. From 1999 to 2018, the proportional use of obesogenic medications increased for anticonvulsants (34.4% to 55.0%) but decreased for antidepressants (32.1% to 18.8%), antidiabetics (82.9% to 52.5%), and beta-blockers (83.9% to 80.7%). The proportional use of obesogenic medications was not associated with weight status, except for antipsychotics. CONCLUSIONS: Use of obesogenic medications was common. Differences in the proportional use of obesogenic medication may reflect changing availability of obesogenic versus nonobesogenic medications over time. The decision to prescribe a nonobesogenic alternative, if one exists, is guided by weighing the risks and benefits of available treatments.

Purpose: The purpose of this study is to examine cross-sectional associations between adverse childhood experiences (ACEs) and mental health among police officers. Design/methodology/approach: The sample was from the Buffalo Cardio-Metabolic Occupational Police Stress study data (132 male and 51 female officers). Standardized surveys were administered to participants. Regression coefficients were obtained from models adjusted for age, sex, race and alcohol intake. All statistical tests were performed using a statistical significance level at p < 0.05. Findings: Regression analyses showed significant positive associations between ACEs and mental health (Posttraumatic Stress Disorder [PTSD]: β = 1.70, p < 0.001 and depressive symptoms: β = 1.29, p < 0.001). Resiliency significantly modified the association between ACEs and PTSD. A positive and significant association was observed among officers with lower resiliency (β = 2.65, p < 0.001). The association between ACEs and PTSD was stronger among male officers compared to females (β = 2.66, p < 0.001 vs. β = 0.59, p ≤ 0.248, respectively). Research limitations/implications: Child abuse and development of PTSD or depression could not be traced through time as this was a cross-sectional study. Recall bias may affect results. Practical implications: PTSD and depression associated with ACEs can affect the interpretation of threat and can exacerbate emotional regulation in officers. An inquiry should be expanded regarding work assignments of victimized officers, such as child exploitation and pornography investigation. Originality/value: There are few studies on ACEs and the mental health of police officers. The present study is among the first to associate multiple police mental health issues with ACEs. © 2021, Emerald Publishing Limited.

PURPOSE: To estimate the prevalence of diagnosis of major eye disorders and their associated payments, in total and per-person diagnosed, among Medicare fee-for-service (FFS) beneficiaries in 2018. METHODS: We analyzed 100% Medicare Part B FFS claims and Part D Events among beneficiaries continuously enrolled for 12 months in 2018 to calculate the proportion of beneficiaries with ≥1 claim indicating age-related macular degeneration (AMD), cataract, diabetic retinopathy (DR), or glaucoma, and their associated payments, including Medicare and patient out-of-pocket. Eye disease and eye care services were identified using case definitions from the Centers for Disease Control and Prevention's (CDC) Vision & Eye Health Surveillance System (VEHSS). Outcomes are reported by disease overall and by age group (0-39, 40-64, 65-84, 85+ years), sex, race/ethnicity, and U.S. state. RESULTS: Among nearly 30 million Medicare Part B FFS beneficiaries in 2018, over 41% (12.4 million) had a claim containing a diagnosis of at least one of the four eye disorders; 33.7% with cataract, 13.3% with glaucoma, 9.2% with AMD and 3.2% with DR. Payments for eye care services and drugs associated with these four conditions were $10.1billion; $3.6 billion for cataract, $3.5 billion for AMD, $2.2 billion for glaucoma and $0.8 billion for DR. The average cost per beneficiary diagnosed was $816: $1,290 for AMD, $781 for DR, $543 for glaucoma, and $360 for cataract. CONCLUSIONS: Major eye disorders are common among Medicare FFS beneficiaries and account for approximately 4.3% of Medicare Part B and 1% of Medicare Part D spending.

OBJECTIVE: To estimate the self-reported prevalence of prescription opioid use and illicit drug use in the U.S. METHODS: Self-reported prescription opioid use and illicit drug use (mostly non-opioid) were obtained for adults and adult workers (NHANES 2005-2016). RESULTS: Prevalence (95% CI) of prescription opioid use was 6.5% (6.0-7.0) (adults) and 4.1% (3.7-4.5) (workers). Prevalence of illicit drug use was 9.5% (8.8-10.1) (adults) and 10.2% (9.4-11.1) (workers). Among occupations, prevalence of prescription opioid use was highest in personal care (6.5%; 4.1-10.4) and healthcare practitioners (5.9%; 3.8-9.0); for illicit drug use, construction/extraction (18.0%; 15.1-21.3) and food preparation (15.8%; 12.5-19.7). CONCLUSION: The prevalence of prescription opioid use was elevated among some occupations. Judicious prescription strategies and targeted interventions are both needed. The prevalence of illicit drug use among certain occupational groups suggests the need to ensure access to therapy.

We examined the association between self-reported sleep quality, sleep duration, and dietary patterns among police officers in the Buffalo Cardio-Metabolic Occupational Stress (BCOPS) study.422 police officers aged 21-74 (2004-2009).We used a cross-sectional study design and obtained sleep quality and duration from responses to the 19-item Pittsburgh Sleep Quality Index. Using 46 energy-adjusted food groups derived from a 125-item food frequency questionnaire, we identified dietary patterns using exploratory factor analysis. Multiple linear regression analysis was used to examine the associations of sleep quality and duration with the derived dietary patterns.We identified major dietary patterns: fruits and vegetables (FV), dairy products, starches and fried foods, and meat and eggs. Individuals with poor sleep quality had a lower average FV score than those with optimal sleep (β [SE] = -0.32 [0.13]; p = .01). Significant interactions were observed between sex and the FV and dairy products dietary patterns, where women with poor sleep quality had a lower mean FV score compared to women with optimal sleep quality (β [SE] = -0.81 [0.29]; p = .01). Women with < 6 hours sleep duration had a lower mean dairy score compared to women with ≥ 7 hours sleep duration (β [SE] = -0.69 [0.29]; p = .02). We did not observe these associations among men.Among women, good sleep quality and long sleep duration were associated with a dietary pattern high in consumption of both fruits and vegetables and dairy products.Abbreviations: BCOPS: Buffalo Cardio-Metabolic Occupational Stress study; BMI: body mass index; FFQ: food frequency questionnaire; FV: fruits and vegetables; KMO: Kaiser-Meyer-Olkin test; MET: metabolic equivalent of task score; PSQI: Pittsburgh Sleep Quality Index questionnaire.

OBJECTIVES: The Abbot ID NOW COVID-19 assay and Quidel Sofia 2 SARS Antigen FIA are point-of-care assays that offer rapid testing for severe acute respiratory syndrome coronavirus 2 viral RNA and nucleocapsid protein, respectively. Given the utility of these devices in the field, we investigated the feasibility and safety of using the ID NOW and Sofia assays in the public health response to the coronavirus disease 2019 pandemic and in future public health emergencies. METHODS: A combination of utilization and contamination testing in addition to a review of instrument workflows was conducted. RESULTS: Utilization testing demonstrated that both tests are intuitive, associated with high user test success (85%) in our study, and could be implemented by staff after minimal training. Contamination tests revealed potential biosafety concerns due to the open design of the ID NOW instrument and the transfer mechanisms with the Sofia. When comparing the workflow of the ID NOW and the Sofia, we found that the ID NOW was more user-friendly and that the transfer technology reduces the chance of contamination. CONCLUSIONS: The ID NOW, Sofia, and other emerging point-of-care tests should be used only after careful consideration of testing workflow, biosafety risk mitigations, and appropriate staff training.

BACKGROUND: The distribution of Clostridioides difficile strains and transmission dynamics in the United States are not well defined. Whole genome sequencing (WGS) across two CDC Emerging Infections Program C. difficile infection (CDI) surveillance regions (Minnesota and New York) was performed to identify predominant multilocus sequence types (MLSTs) in community and healthcare-associated disease and assess transmission. METHODS: C. difficile isolates from CDI cases over three months between 2016 and 2017 underwent WGS. Cases were residents of the catchment area without a positive C. difficile test in the preceding 8 weeks. Cases were epidemiologically classified as healthcare (HCA) or community (CA) associated. RESULTS: Of 422 isolates, 212 (50.2%) were HCA and 203 (48.1%) were CA. Predominant MLSTs were ST42 (9.3%), ST8 (7.8%), and ST2 (8.1%). MLSTs associated with HCA-CDI included ST1 (76%), ST53 (83.3%), ST43 (80.0%), while those associated with CA-CDI included ST3 (76.9%), and ST41 (77.8%). ST1 was more frequent in NY than MN (10.8% vs 3.1%). Thirty three pairs were closely related genomically, 14 of which had potential patient-patient transmission supported by chart review. DISCUSSION: The genomic epidemiology of C. difficile across two regions of the US indicates the presence of a diverse strain profile and limited direct transmission.