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Understanding the risk of infection from household- and community-exposures and the transmissibility of asymptomatic infections is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. We apply household transmission models to data from a cross-sectional, household-based population serosurvey of 4,534 people ≥5 years from 2,267 households enrolled April-June 2020 in Geneva, Switzerland. We found that the risk of infection from exposure to a single infected household member aged ≥5 years (17.3%,13.7-21.7) was more than three-times that of extra-household exposures over the first pandemic wave (5.1%,4.5-5.8). Young children had a lower risk of infection from household members. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.4% lower odds (95%CrI,31.8-88.8%) of infecting another household member compared to those reporting symptoms, accounting for 14.5% (95%CrI, 7.2-22.7%) of all household infections., Household-based studies can provide insights into SARS-CoV-2 transmission. Here, the authors fit transmission models to serological data from Geneva, Switzerland, and estimate that the risk of infection from single household exposure (17.3%) was higher than for extra-household exposure (5.1%).

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Adverse Childhood Experiences have been associated with an increased risk for various health and mental health challenges in adulthood, which shed new light on positive parenting as important preventive healthcare. Although behavioral parenting interventions have been shown to be effective in promoting positive parenting and reducing harsh discipline, beneficiaries have been limited to parents with identified needs. Lately, primary care has been increasingly recognized as an ideal platform to disseminate evidence-based positive discipline strategies at the population level. Behavior Checker® is a multi-component primary care-based parenting intervention, seeking to maximize the utility of primary care in population-based positive parenting promotion. The purpose of this study was to examine the effects of Behavior Checker® training on pediatric staff’s perceived knowledge, attitude, and confidence in educating parents about positive discipline strategies. Pre- and post-training surveys were administered to staff who participated in the Behavior Checker® training in a university-affiliated pediatric clinic located in a Midwestern metropolitan region. A paired t-test and Wilcoxon signed-rank tests were conducted to compare pre- and post-training scores in the perceived knowledge and confidence in educating parents about positive discipline strategies to manage common behavioral issues of children. The level of perceived knowledge and confidence significantly increased following staff’s participation in the training. A larger study is warranted.

Summary Background Pandemic Vibrio cholerae from cholera-endemic countries around the Bay of Bengal regularly seed epidemics globally. Without reducing cholera in these countries, including Bangladesh, global cholera control might never be achieved. Little is known about the geographical distribution and magnitude of V cholerae O1 transmission nationally. We aimed to describe infection risk across Bangladesh, making use of advances in cholera seroepidemiology, therefore overcoming many of the limitations of current clinic-based surveillance. Methods We tested serum samples from a nationally representative serosurvey in Bangladesh with eight V cholerae-specific assays. Using these data with a machine-learning model previously validated within a cohort of confirmed cholera cases and their household contacts, we estimated the proportion of the population with evidence of infection by V cholerae O1 in the previous year (annual seroincidence) and used Bayesian geostatistical models to create high-resolution national maps of infection risk. Findings Between Oct 16, 2015, and Jan 24, 2016, we obtained and tested serum samples from 2930 participants (707 households) in 70 communities across Bangladesh. We estimated national annual seroincidence of V cholerae O1 infection of 17·3% (95% CI 10·5–24·1). Our high-resolution maps showed large heterogeneity of infection risk, with community-level annual infection risk within the sampled population ranging from 4·3% to 62·9%. Across Bangladesh, we estimated that 28·1 (95% CI 17·1–39·2) million infections occurred in the year before the survey. Despite having an annual seroincidence of V cholerae O1 infection lower than much of Bangladesh, Dhaka (the capital of Bangladesh and largest city in the country) had 2·0 (95% CI 0·6–3·9) million infections during the same year, primarily because of its large population. Interpretation Serosurveillance provides an avenue for identifying areas with high V cholerae O1 transmission and investigating key risk factors for infection across geographical scales. Serosurveillance could serve as an important method for countries to plan and monitor progress towards 2030 cholera elimination goals. Funding The Bill & Melinda Gates Foundation, National Institutes of Health, and US Centers for Disease Control and Prevention.

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. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk 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.

Background: Tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on reverse transcriptase polymerase chain reaction (RT-PCR) are being used to “rule out” infection among high-risk persons, such as exposed inpatients and health care workers. It is critical to understand how the predictive value of the test varies with time from exposure and symptom onset to avoid being falsely reassured by negative test results. Objective: To estimate the false-negative rate by day since infection. Design: Literature review and pooled analysis. Setting: 7 previously published studies providing data on RT-PCR performance by time since symptom onset or SARS-CoV-2 exposure using samples from the upper respiratory tract (n = 1330). Patients: A mix of inpatients and outpatients with SARS-CoV-2 infection. Measurements: A Bayesian hierarchical model was fitted to estimate the false-negative rate by day since exposure and symptom onset. Results: Over the 4 days of infection before the typical time of symptom onset (day 5), the probability of a false-negative result in an infected person decreases from 100% (95% CI, 100% to 100%) on day 1 to 67% (CI, 27% to 94%) on day 4. On the day of symptom onset, the median false-negative rate was 38% (CI, 18% to 65%). This decreased to 20% (CI, 12% to 30%) on day 8 (3 days after symptom onset) then began to increase again, from 21% (CI, 13% to 31%) on day 9 to 66% (CI, 54% to 77%) on day 21. Limitation: Imprecise estimates due to heterogeneity in the design of studies on which results were based. Conclusion: Care must be taken in interpreting RT-PCR tests for SARS-CoV-2 infection—particularly early in the course of infection—when using these results as a basis for removing precautions intended to prevent onward transmission. If clinical suspicion is high, infection should not be ruled out on the basis of RT-PCR alone, and the clinical and epidemiologic situation should be carefully considered. Primary Funding Source: National Institute of Allergy and Infectious Diseases, Johns Hopkins Health System, and U.S. Centers for Disease Control and Prevention., Tests for severe acute respiratory syndrome coronavirus 2 based on reverse transcriptase polymerase chain reaction (RT-PCR) are being used to “rule out” infection among high-risk persons, such as exposed inpatients and health care workers, but studies suggest that test sensitivity may be low. This study estimates the false-negative rate by day since exposure to infection by pooling and modeling data from previously published studies on RT-PCR sensitivity of upper respiratory tract samples of persons who were ultimately confirmed to have coronavirus disease 19.

Background A surveillance system that is sensitive to detecting high burden areas is critical for achieving widespread disease control. In 2014, Bangladesh established a nationwide, facility-based cholera surveillance system for Vibrio cholerae infection. We sought to measure the sensitivity of this surveillance system to detect cases to assess whether cholera elimination targets outlined by the Bangladesh national control plan can be adequately measured. Methods We overlaid maps of nationally representative annual V cholerae seroincidence onto maps of the catchment areas of facilities where confirmatory laboratory testing for cholera was conducted, and we identified its spatial complement as surveillance greyspots, areas where cases likely occur but go undetected. We assessed surveillance system sensitivity and changes to sensitivity given alternate surveillance site selection strategies. Results We estimated that 69% of Bangladeshis (111.7 million individuals) live in surveillance greyspots and that 23% (25.5 million) of these individuals live in areas with the highest V cholerae infection rates. Conclusions The cholera surveillance system in Bangladesh has the ability to monitor progress towards cholera elimination goals among 31% of the country’s population, which may be insufficient for accurately measuring progress. Increasing surveillance coverage, particularly in the highest risk areas, should be considered.

Significance Forecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts., A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.

Introduction. Smokefree for Kansas Kids is a program designedto train pediatric clinic staff to assess for tobaccoexposure and provide brief smoking cessation interventionsto caregivers and patients. The purpose of this studywas to evaluate the impact of this program and improvefuture tobacco intervention efforts in pediatric clinics. Methods. Eighty-six pediatric physicians and staff attendedat least one of three training sessions. A randomsample of pediatric medical records was selected pre-intervention(n = 49) and post-intervention (n = 150). Electronicmedical records were reviewed to assess for documentationof tobacco use intervention implemented in the clinic. Results. Of the 199 pediatric clinic visits reviewed, 197 metthe study criteria. All but one visit documented an assessmentof tobacco exposure. Among children exposed to tobacco (n= 42), providers were more likely to discuss tobacco use withcaregivers post-intervention (35.7%) compared to pre-intervention(7.1%; p < 0.05). One in five caregivers in the postinterventiongroup were advised to quit (21.4%) compared tothe pre-intervention group (7.1%). In the post-interventiongroup, 14.3% were referred to the state quitline compared tono referrals in the pre-intervention group. The difference inrates for providing advice and referral between pre-interventionand post-intervention were not statistically significant. Conclusions. Implementation of the Smoke Free for Kansas Kidsintervention was associated with modest improvements in clinictobacco intervention efforts, but many patients still failed toreceive optimal assessments or interventions. Additional effortsmay be needed to enhance this program. KS J Med 2017;10(1):7-11.

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. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger 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. Significance Statement This 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 US. 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.

Forecasting transmission of infectious diseases, especially for vector-borne diseases, poses unique challenges for researchers. Behaviors of and interactions between viruses, vectors, hosts, and the environment each play a part in determining the transmission of a disease. Public health surveillance systems and other sources provide valuable data that can be used to accurately forecast disease incidence. However, many aspects of common infectious disease surveillance data are imperfect: cases may be reported with a delay or in some cases not at all, data on vectors may not be available, and case data may not be available at high geographical or temporal resolution. In the face of these challenges, researchers must make assumptions to either account for these underlying processes in a mechanistic model or to justify their exclusion altogether in a statistical model.

BackgroundKnowing the transmissibility of asymptomatic infections and risk of infection from household- and community-exposures is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals.ObjectiveEstimate the risk of SARS-CoV-2 infection from household and community exposures, and identify key risk factors for transmission and infection.DesignCross-sectional household serosurvey and transmission model.SettingGeneva, SwitzerlandParticipants4,524 household members ≥5 years from 2,267 households enrolled April-June 2020.MeasurementsPast SARS-CoV-2 infection confirmed through IgG ELISA. Chain-binomial models based on the number of infections within households used to estimate the cumulative extra-household infection risk and infection risk from exposure to an infected household member by demographics and infector’s symptoms.ResultsThe chance of being infected by a SARS-CoV-2 infected household member was 17.3% (95%CrI,13.7-21.7%) compared to a cumulative extra-household infection risk of 5.1% (95%CrI,4.5-5.8%). Infection risk from an infected household member increased with age, with 5-9 year olds having 0.4 times (95%CrI, 0.07-1.4) the odds of infection, and ≥65 years olds having 2.7 (95%CrI,0.88-7.4) times the odds of infection of 20-49 year olds. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.6% lower odds (95%CrI,33.7-88.1%) of infecting another household member compared to those reporting symptoms, accounting for 14.7% (95%CrI,6.3-23.2%) of all household infections.LimitationsSelf-reported symptoms, small number of seropositive kids and imperfect serologic tests.ConclusionThe risk of infection from exposure to a single infected household member was more than three-times that of extra-household exposures over the first pandemic wave. Young children had a lower risk of infection from household members. Asymptomatic infections are far less likely to transmit than symptomatic ones but do cause infections.Funding SourceSwiss Federal Office of Public Health, Swiss School of Public Health (Corona Immunitas research program), Fondation de Bienfaisance du Groupe Pictet, Fondation Ancrage, Fondation Privée des Hôpitaux Universitaires de Genève, and Center for Emerging Viral Diseases.

IgM and IgA responses to SARS-CoV-2 RBD in severe COVID patients decay rapidly, while IgG responses persist for over 3 months., We measured plasma and/or serum antibody responses to the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 in 343 North American patients infected with SARS-CoV-2 (of which 93% required hospitalization) up to 122 days after symptom onset and compared them to responses in 1548 individuals whose blood samples were obtained prior to the pandemic. After setting seropositivity thresholds for perfect specificity (100%), we estimated sensitivities of 95% for IgG, 90% for IgA, and 81% for IgM for detecting infected individuals between 15 and 28 days after symptom onset. While the median time to seroconversion was nearly 12 days across all three isotypes tested, IgA and IgM antibodies against RBD were short-lived with median times to seroreversion of 71 and 49 days after symptom onset. In contrast, anti-RBD IgG responses decayed slowly through 90 days with only 3 seropositive individuals seroreverting within this time period. IgG antibodies to SARS-CoV-2 RBD were strongly correlated with anti-S neutralizing antibody titers, which demonstrated little to no decrease over 75 days since symptom onset. We observed no cross-reactivity of the SARS-CoV-2 RBD-targeted antibodies with other widely circulating coronaviruses (HKU1, 229 E, OC43, NL63). These data suggest that RBD-targeted antibodies are excellent markers of previous and recent infection, that differential isotype measurements can help distinguish between recent and older infections, and that IgG responses persist over the first few months after infection and are highly correlated with neutralizing antibodies.

BACKGROUNDCharacterizing the humoral immune response to SARS-CoV-2 and developing accurate serologic assays are needed for diagnostic purposes and estimating population-level seroprevalence.METHODSWe measured the kinetics of early antibody responses to the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 in a cohort of 259 symptomatic North American patients infected with SARS-CoV-2 (up to 75 days after symptom onset) compared to antibody levels in 1548 individuals whose blood samples were obtained prior to the pandemic.RESULTSBetween 14-28 days from onset of symptoms, IgG, IgA, or IgM antibody responses to RBD were all accurate in identifying recently infected individuals, with 100% specificity and a sensitivity of 97%, 91%, and 81% respectively. Although the estimated median time to becoming seropositive was similar across isotypes, IgA and IgM antibodies against RBD were short-lived with most individuals estimated to become seronegative again by 51 and 47 days after symptom onset, respectively. IgG antibodies against RBD lasted longer and persisted through 75 days post-symptoms. IgG antibodies to SARS-CoV-2 RBD were highly correlated with neutralizing antibodies targeting the S protein. No cross-reactivity of the SARS-CoV-2 RBD-targeted antibodies was observed with several known circulating coronaviruses, HKU1, OC 229 E, OC43, and NL63.CONCLUSIONSAmong symptomatic SARS-CoV-2 cases, RBD-targeted antibodies can be indicative of previous and recent infection. IgG antibodies are correlated with neutralizing antibodies and are possibly a correlate of protective immunity.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

BackgroundAssessing the burden of COVID-19 based on medically-attended case counts is suboptimal given its reliance on testing strategy, changing case definitions and the wide spectrum of disease presentation. Population-based serosurveys provide one avenue for estimating infection rates and monitoring the progression of the epidemic, overcoming many of these limitations.MethodsTaking advantage of a pool of adult participants from population-representative surveys conducted in Geneva, Switzerland, we implemented a study consisting of 8 weekly serosurveys among these participants and their household members older than 5 years. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lübeck, Germany). We estimated seroprevalence using a Bayesian regression model taking into account test performance and adjusting for the age and sex of Geneva’s population.ResultsIn the first three weeks, we enrolled 1335 participants coming from 633 households, with 16% InterpretationAssuming that the presence of IgG antibodies is at least in the short-term associated with immunity, these results highlight that the epidemic is far from burning out simply due to herd immunity. Further, no differences in seroprevalence between children and middle age adults are observed. These results must be considered as Switzerland and the world look towards easing restrictions aimed at curbing transmission.

SARS-CoV-2 RT-PCR based tests are being used to “rule out” infection among high-risk individuals such as exposed inpatients and healthcare workers. It is critical to understand how the predictive value of the test varies with time from exposure and symptom onset in order to avoid being falsely reassured by negative tests. As such, the goal of our study was to estimate the false negative rate by day since infection. We used previously published data on RT-PCR sensitivity on samples derived from nasal swabs by day since symptom onset (n=633) and fit a cubic polynomial spline to calculate the false negative rate by day since exposure and symptom onset. Over the four days of infection prior to the typical time of symptom onset (day 5) the probability of a false negative test in an infected individual falls from 100% on day one (95% CI 69-100%) to 61% on day four (95% CI 18-98%), though there is considerable uncertainty in these numbers. On the day of symptom onset, the median false negative rate was 39% (95% CI 16-77%). This decreased to 26% (95% CI 18-34%) on day 8 (3 days after symptom onset), then began to rise again, from 27% (95% CI 20-34%) on day 9 to 61% (95% CI 54-67%) on day 21. Care must be taken when interpreting RT-PCR tests for SARS-CoV-2 infection, particularly if performed early in the course of infection, when using these results as a basis for removing precautions intended to prevent onward transmission. If there is high clinical suspicion, patients should not be ruled out on the basis of RT-PCR alone, and the clinical and epidemiologic situation should be carefully considered.

Background COVID-19 could have even more dire consequences in refugees camps than in general populations. Bangladesh has confirmed COVID-19 cases and hosts almost 1 million Rohingya refugees from Myanmar, with 600,000 concentrated in the Kutupalong-Balukhali Expansion Site (mean age, 21 years; standard deviation [SD], 18 years; 52% female). Projections of the potential COVID-19 burden, epidemic speed, and healthcare needs in such settings are critical for preparedness planning. Methods and findings To explore the potential impact of the introduction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Kutupalong-Balukhali Expansion Site, we used a stochastic Susceptible Exposed Infectious Recovered (SEIR) transmission model with parameters derived from emerging literature and age as the primary determinant of infection severity. We considered three scenarios with different assumptions about the transmission potential of SARS-CoV-2. From the simulated infections, we estimated hospitalizations, deaths, and healthcare needs expected, age-adjusted for the Kutupalong-Balukhali Expansion Site age distribution. Our findings suggest that a large-scale outbreak is likely after a single introduction of the virus into the camp, with 61%–92% of simulations leading to at least 1,000 people infected across scenarios. On average, in the first 30 days of the outbreak, we expect 18 (95% prediction interval [PI], 2–65), 54 (95% PI, 3–223), and 370 (95% PI, 4–1,850) people infected in the low, moderate, and high transmission scenarios, respectively. These reach 421,500 (95% PI, 376,300–463,500), 546,800 (95% PI, 499,300–567,000), and 589,800 (95% PI, 578,800–595,600) people infected in 12 months, respectively. Hospitalization needs exceeded the existing hospitalization capacity of 340 beds after 55–136 days, between the low and high transmission scenarios. We estimate 2,040 (95% PI, 1,660–2,500), 2,650 (95% PI, 2,030–3,380), and 2,880 (95% PI, 2,090–3,830) deaths in the low, moderate, and high transmission scenarios, respectively. Due to limited data at the time of analyses, we assumed that age was the primary determinant of infection severity and hospitalization. We expect that comorbidities, limited hospitalization, and intensive care capacity may increase this risk; thus, we may be underestimating the potential burden. Conclusions Our findings suggest that a COVID-19 epidemic in a refugee settlement may have profound consequences, requiring large increases in healthcare capacity and infrastructure that may exceed what is currently feasible in these settings. Detailed and realistic planning for the worst case in Kutupalong-Balukhali and all refugee camps worldwide must begin now. Plans should consider novel and radical strategies to reduce infectious contacts and fill health worker gaps while recognizing that refugees may not have access to national health systems., Paul Spiegel and colleagues estimate the potential consequences of COVID-19 outbreaks at refugee camps., Author summary Why was this study done? Forcibly displaced populations, especially those who reside in settlements with high density, poor access to water and sanitation, and limited health services, are especially vulnerable to COVID-19. Bangladesh, which has confirmed COVID-19 cases, hosts almost 900,000 Rohingya refugees from Myanmar in the Cox’s Bazar district, approximately 600,000 of whom are concentrated in the Kutupalong-Balukhali Expansion Site. The capacity to meet the existing health needs of this population is limited; an outbreak of COVID-19 within this population threatens to severely disrupt an already fragile situation. We conducted this study to estimate the number of people infected, hospitalizations, and deaths that might occur in the Kutupalong-Balukhali Expansion Site to inform ongoing preparedness and response activities by the Bangladesh government, the United Nations agencies, and other national and international actors. What did the researchers do and find? Using a dynamic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, we simulated how a COVID-19 outbreak could spread within the expansion site according to three possible transmission scenarios (high, moderate, and low). Our results suggest that a large-scale outbreak is very likely in this setting after a single infectious person enters the camp, with 0.5%–91% of the population expected to be infected within the first three months and over 70%–98% during the first year, depending on the transmission scenario, should no effective interventions be put into place. Hospitalization needs may exceed the existing hospitalization capacity of 340 beds 55–136 days after introduction. What do these findings mean? A COVID-19 epidemic in a high–population density refugee settlement may have profound consequences, requiring increases in healthcare capacity and infrastructure that exceed what is feasible in this setting. As many of the approaches used to prevent and respond to COVID-19 in the most affected areas so far will not be practical in humanitarian settings, novel and untested strategies to protect the most vulnerable population groups should be considered, as well as innovative solutions to fill health workforce gaps.

BackgroundCOVID-19 could have even more dire consequences in refugees camps than in general populations. Bangladesh has confirmed COVID-19 cases and hosts almost 1 million Rohingya refugees from Myanmar with 600,000 concentrated in Kutupalong-Balukhali Expansion Site (age mean: 21 years, sd: 18 years, 52% female). Projections of the potential COVID-19 burden, epidemic speed, and healthcare needs in such settings are critical for preparedness planning.Methods and FindingsTo explore the potential impact of the introduction of SARS-CoV-2 in Kutupalong-Balukhali Expansion Site, we used a stochastic SEIR transmission model with parameters derived from emerging literature and age as the primary determinant of infection severity. We considered three scenarios with different assumptions about the transmission potential of SARS-CoV-2. From the simulated infections, we estimated hospitalizations, deaths, and healthcare needs expected, age-adjusted for the Kutupalong-Balukhali Expansion Site age distribution.Our findings suggest that a large-scale outbreak is likely after a single introduction of the virus into the camp with 61-92% of simulations leading to at least 1,000 people infected across scenarios. On average, in the first 30 days of the outbreak, we expect 18 (95% prediction interval (PI), 2-65), 54 (95% PI, 3-223), and 370 (95% PI, 4-1,850) people infected in the low, moderate, and high transmission scenarios, respectively. These reach 421,500 (95% PI, 376,300-463,500), 546,800 (95% PI, 499,300-567,000) and 589,800 (95% PI, 578,800-595,600) people infected in 12 months, respectively. Hospitalization needs exceeded the existing hospitalization capacity of 340 beds after 55-136 days between the low and high transmission scenarios. We estimate 2,040 (95% PI, 1,660-2,500), 2,650 (95% PI, 2,030-3,380), and 2,880 (95% PI, 2,090-3,830) deaths in the low, moderate and high transmission scenarios, respectively.Due to limited data at the time of analyses, we assumed that age was the primary determinant of infection severity and hospitalization. We expect that comorbidities and limited hospitalization and intensive care capacity may increase this risk, thus we may be underestimating the potential burden.ConclusionsOur findings suggest that a COVID-19 epidemic in a refugee settlement may have profound consequences, requiring large increases in healthcare capacity and infrastructure that may exceed what is currently feasible in these settings. Detailed and realistic planning for the worst-case in Kutupalong-Balukhali and all refugee camps worldwide must begin now. Plans should consider novel and radical strategies to reduce infectious contacts and fill health worker gaps while recognizing that refugees may not have access to national health systems.AUTHORS’ SUMMARYWhy was this study done?Forcibly displaced populations, especially those who reside in settlements with high density, poor access to water and sanitation, and limited health services, are especially vulnerable to COVID-19.Bangladesh, which has confirmed COVID-19 cases, hosts almost 900,000 Rohingya refugees from Myanmar in the Cox’s Bazar district, approximately 600,000 of whom are concentrated in the Kutupalong-Balukhali Expansion Site.The capacity to meet the existing health needs of this population is limited; an outbreak of COVID-19 within this population threatens to severely disrupt an already fragile situation.We conducted this study to estimate the number of people infected, hospitalizations, and deaths that might occur in the Kutupalong-Balukhali Expansion Site to inform ongoing preparedness and response activities by the Bangladesh government, the United Nations agencies, and other national and international actors.What did the researchers do and find?Using a dynamic model of SARS-CoV-2 transmission, we simulated how a COVID-19 outbreak could spread within the Expansion Site according to three possible transmission scenarios (high, moderate, and low).Our results suggest that a large-scale outbreak is very likely in this setting after a single infectious person enters the camp, with 0.5-91% of the population expected to be infected within the first three months and over 70-98% during the first year depending on the transmission scenario, should no effective interventions be put into place.Hospitalization needs may exceed the existing hospitalization capacity of 340 beds after 55-136 days of introduction.What do these findings mean?A COVID-19 epidemic in a high population density refugee settlement may have profound consequences, requiring increases in healthcare capacity and infrastructure that exceed what is feasible in this setting.As many of the approaches used to prevent and respond to COVID-19 in the most affected areas so far will not be practical in humanitarian settings, novel and untested strategies to protect the most vulnerable population groups should be considered, as well as innovative solutions to fill health workforce gaps.

There has been a growing trend of local and national coverage of and interest in concussion injuries over the past 2 decades. Increasing public concern over potential catastrophic and unknown long-term effects of sports-related concussion injuries has led to an acknowledgment of the strong public health need for addressing all concussion injuries, regardless of mechanism of injury. In efforts to address this need for concussion prevention and management, both in sports and nonsports, The University of Kansas Health System initiated the interdisciplinary Center for Concussion Management program in 2012. The program was created as a virtual clinic concept and includes voluntary participation from various providers across the institution, limited budget, and space obstacles. Since its inception, the program has continued to operate as its initial design of a multidisciplinary team model outside the sole ownership of 1 department, and has expanded to include education and outreach to local and regional schools and groups.

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.To estimate the length of the incubation period of COVID-19 and describe its public health implications.Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

A novel human coronavirus (2019-nCoV) was identified in China in December, 2019. There is limited support for many of its key epidemiologic features, including the incubation period, which has important implications for surveillance and control activities. Here, we use data from public reports of 101 confirmed cases in 38 provinces, regions, and countries outside of Wuhan (Hubei province, China) with identifiable exposure windows and known dates of symptom onset to estimate the incubation period of 2019-nCoV. We estimate the median incubation period of 2019-nCoV to be 5.2 days (95% CI: 4.4, 6.0), and 97.5% of those who develop symptoms will do so within 10.5 days (95% CI: 7.3, 15.3) of infection. These estimates imply that, under conservative assumptions, 64 out of every 10,000 cases will develop symptoms after 14 days of active monitoring or quarantine. Whether this risk is acceptable depends on the underlying risk of infection and consequences of missed cases. The estimates presented here can be used to inform policy in multiple contexts based on these judgments.

Summary Background Assessing the burden of COVID-19 on the basis of medically attended case numbers is suboptimal given its reliance on testing strategy, changing case definitions, and disease presentation. Population-based serosurveys measuring anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies provide one method for estimating infection rates and monitoring the progression of the epidemic. Here, we estimate weekly seroprevalence of anti-SARS-CoV-2 antibodies in the population of Geneva, Switzerland, during the epidemic. Methods The SEROCoV-POP study is a population-based study of former participants of the Bus Santé study and their household members. We planned a series of 12 consecutive weekly serosurveys among randomly selected participants from a previous population-representative survey, and their household members aged 5 years and older. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available ELISA. We estimated seroprevalence using a Bayesian logistic regression model taking into account test performance and adjusting for the age and sex of Geneva's population. Here we present results from the first 5 weeks of the study. Findings Between April 6 and May 9, 2020, we enrolled 2766 participants from 1339 households, with a demographic distribution similar to that of the canton of Geneva. In the first week, we estimated a seroprevalence of 4·8% (95% CI 2·4–8·0, n=341). The estimate increased to 8·5% (5·9–11·4, n=469) in the second week, to 10·9% (7·9–14·4, n=577) in the third week, 6·6% (4·3–9·4, n=604) in the fourth week, and 10·8% (8·2–13·9, n=775) in the fifth week. Individuals aged 5–9 years (relative risk [RR] 0·32 [95% CI 0·11–0·63]) and those older than 65 years (RR 0·50 [0·28–0·78]) had a significantly lower risk of being seropositive than those aged 20–49 years. After accounting for the time to seroconversion, we estimated that for every reported confirmed case, there were 11·6 infections in the community. Interpretation These results suggest that most of the population of Geneva remained uninfected during this wave of the pandemic, despite the high prevalence of COVID-19 in the region (5000 reported clinical cases over

The infection fatality risk (IFR) is the average number of deaths per infection by a pathogen and is key to characterizing the severity of infection across the population and for specific demographic groups. To date, there are few empirical estimates of IFR published due to challenges in measuring infection rates.1,2 Outside of closed, closely surveilled populations where infection rates can be monitored through viral surveillance, we must rely on indirect measures of infection, like specific antibodies. Representative seroprevalence studies provide an important avenue for estimating the number of infections in a community, and when combined with death counts can lead to robust estimates of the IFR. We estimated overall and age-specific IFR for the canton of Geneva, Switzerland using age-stratified daily case and death incidence reports combined with five weekly population-based seroprevalence estimates.3 From February 24th to June 2nd there were 5’039 confirmed cases and 286 reported deaths within Geneva (population of 506’765). We inferred age-stratified (5-9, 10-19, 20-49, 50-65 and 65+) IFRs by linking the observed number of deaths to the estimated number of infected individuals from each serosurvey. We account for the delays between infection and seroconversion as well as between infection and death.4 Inference is drawn in a Bayesian framework that incorporates uncertainty in seroprevalence estimates (supplement).Of the 286 reported deaths caused by SARS-CoV-2, the youngest person to die was 31 years old. Infected individuals younger than 50 years experienced statistically similar IFRs (range 0.00032-0.0016%), which increases to 0.14% (95% CrI 0.096-0.19) for those 50-64 years old to 5.6% (95% CrI 4.3-7.4) for those 65 years and older (supplement). After accounting for demography and age-specific seroprevalence, we estimate a population-wide IFR of 0.64% (95% CrI 0.38-0.98).Our results are subject to two notable limitations. Among the 65+ age group that died of COVID-19 within Geneva, 50% were reported among residents of assisted care facilities, where around 0.8% of the Geneva population resides. While the serosurvey protocol did not explicitly exclude these individuals, they are likely to have been under-represented. This would lead to an overestimation of the IFR in the 65+ age group if seroprevalence in this institutionalized population was higher than in the general population (supplement). Further, our IFR estimates are based on current evidence regarding post-infection antibody kinetics, which may differ between severe and mild infections. If mild infections have significantly lower and short-lived antibody responses, our estimates of IFR may be biased upwards.5Estimates of IFR are key for understanding the true pandemic burden and for weighing different risk reduction strategies. The IFR is not solely determined by host and pathogen biology, but also by the capacity of health systems to treat severe cases. Despite having among the highest per capita incidence in Switzerland, Geneva’s health system accommodated the influx of cases needing intensive care (peak of 80/110 ICU-beds including surge capacity) while maintaining care quality standards. As such, our IFR estimates can be seen as a best-case scenario with respect to health system capacity. Our results reveal that population-wide estimates of IFR mask great heterogeneity by age and point towards the importance of age-targeted interventions to reduce exposures among those at highest risk of death.

Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This strategy enables us to create predictions for the timing of and incidence in the peak week of the season. Our implementation of KCDE incorporates 2 novel kernel components: a periodic component that captures seasonality in disease incidence and a component that allows for a full parameterization of the bandwidth matrix with discrete variables. We demonstrate via simulation that a fully parameterized bandwidth matrix can be beneficial for estimating conditional densities. We apply the method to predicting dengue fever and influenza and compare to a seasonal autoregressive integrated moving average model and HHH4, a previously published extension to the generalized linear model framework developed for infectious disease incidence. The KCDE outperforms the baseline methods for predictions of dengue incidence in individual weeks. The KCDE also offers more consistent performance than the baseline models for predictions of incidence in the peak week and is comparable to the baseline models on the other prediction targets. Using the periodic kernel function led to better predictions of incidence. Our approach and extensions of it could yield improved predictions for public health decision makers, particularly in diseases with heterogeneous seasonal dynamics such as dengue fever.

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious dis-eases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children’s Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence.

Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this paper, we present a framework for evaluating time series predictions that emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naïve reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this framework facilitates comparisons of one or more models’ predictive performance across different sets of data. We illustrate the use of this metric with a case study comparing predictions of dengue hemorrhagic fever incidence in two provinces of Thailand. This example demonstrates the utility and interpretability of the relative mean absolute error metric in practice, and underscores the practical advantages of using relative performance metrics when evaluating predictions.

Introduction Smokefree for Kansas Kids is a program designed to train pediatric clinic staff to assess for tobacco exposure and provide brief smoking cessation interventions to caregivers and patients. The purpose of this study was to evaluate the impact of this program and improve future tobacco intervention efforts in pediatric clinics. Method Eighty-six pediatric physicians and staff attended at least one of three training sessions. A random sample of pediatric medical records was selected pre-intervention (n = 49) and post-intervention (n = 150). Electronic medical records were reviewed to assess for documentation of tobacco use intervention implemented in the clinic. Results Of the 199 pediatric clinic visits reviewed, 197 met the study criteria. All but one visit documented an assessment of tobacco exposure. Among children exposed to tobacco (n = 42), providers were more likely to discuss tobacco use with caregivers post-intervention (35.7%) compared to pre-intervention (7.1%; p < 0.05). One in five caregivers in the post-intervention group were advised to quit (21.4%) compared to the pre-intervention group (7.1%). In the post-intervention group, 14.3% were referred to the state quitline compared to no referrals in the pre-intervention group. The difference in rates for providing advice and referral between pre-intervention and post-intervention were not statistically significant. Conclusions Implementation of the Smoke Free for Kansas Kids intervention was associated with modest improvements in clinic tobacco intervention efforts, but many patients still failed to receive optimal assessments or interventions. Additional efforts may be needed to enhance this program.

Significance Dengue hemorrhagic fever poses a major problem for public health officials in Thailand. The number and location of cases vary dramatically from year to year, which makes planning prevention and treatment activities before the dengue season difficult. We develop statistical models with biologically motivated covariates to make forecasts for each Thai province every year. The forecasts from our models have less error than those of a baseline model on out-of-sample data. Furthermore, the forecasts from a model based on incidence occurring before the start of the rainy season successfully order provinces by outbreak risk. These early, accurate forecasts of dengue hemorrhagic fever incidence could help public health officials determine where to allocate their resources in the future., Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000–2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010–2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.

Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This strategy enables us to create predictions for the timing of and incidence in the peak week of the season. Our implementation of KCDE incorporates two novel kernel components: a periodic component that captures seasonality in disease incidence, and a component that allows for a full parameterization of the bandwidth matrix with discrete variables. We demonstrate via simulation that a fully parameterized bandwidth matrix can be beneficial for estimating conditional densities. We apply the method to predicting dengue fever and influenza, and compare to a seasonal autoregressive integrated moving average (SARIMA) model and HHH4, a previously published extension to the generalized linear model framework developed for infectious disease incidence. KCDE outperforms the baseline methods for predictions of dengue incidence in individual weeks. KCDE also offers more consistent performance than the baseline models for predictions of incidence in the peak week, and is comparable to the baseline models on the other prediction targets. Using the periodic kernel function led to better predictions of incidence. Our approach and extensions of it could yield improved predictions for public health decision makers, particularly in diseases with heterogeneous seasonal dynamics such as dengue fever.

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making., Author Summary Predicting the course of infectious disease outbreaks in real-time is a challenging task. It requires knowledge of the particular disease system as well as a pipeline that can turn raw data from a public health surveillance system into calibrated predictions of disease incidence. Dengue is a mosquito-borne infectious disease that places an immense public health and economic burden upon countries around the world, especially in tropical areas. In 2014 our research team, a collaboration of the Ministry of Public Health of Thailand and academic researchers from the United States, implemented a system for generating real-time forecasts of dengue hemorrhagic fever based on the disease surveillance reports from Thailand. We compared predictions from several different statistical models, identifying locations and times where our predictions were accurate. We also quantified the extent to which delayed reporting of cases in real-time impacted our predictions. Broadly speaking, improving real-time predictions can enable more targeted, timely interventions and risk communication, both of which have a measurable impact on disease spread in epidemic and pandemic scenarios. It is vital that we continue to build knowledge about the best ways to make these forecasts and integrate them into public health decision making.

Real-time surveillance of an infectious disease in a third world country poses many problems that are not conventionally confronted by statistical researchers. As the first ones - to our knowledge - to attempt real-time forecasts of dengue fever in Thailand, we have faced these problems head-on in our quest to build a model that accurately predicts case counts in the presence of erratic reporting, shifting population dynamics, and potential climate change.

Purpose To determine in a randomized comparison whether combination chemotherapy with high-dose cisplatin (HDPEB) improves the event-free (EFS) and overall (OS) survival of children and adolescents with high-risk malignant germ cell tumors (MGCT) as compared with standard-dose cisplatin (PEB) and to compare the regimens' toxicity. Patients and Methods Between March 1990 and February 1996, 299 eligible patients with stage III and IV gonadal and extragonadal (all stages) MGCT were enrolled onto this Pediatric Oncology Group and Children's Cancer Group study. Chemotherapy included bleomycin 15 units/m2 on day 1, etoposide 100 mg/m2 on days 1 through 5, and either high-dose cisplatin 40 mg/m2 on days 1 through 5 (HDPEB; n = 149) or standard-dose cisplatin 20 mg/m2 on days 1 through 5 (PEB; n = 150). Patients were evaluated after four cycles of therapy, and those with residual disease underwent surgery. Those with malignant disease in resected specimen received two additional cycles of their assigned regimen. Results One hundred thirty-four eligible patients with advanced testicular (n = 60) or ovarian (n = 74) tumors and 165 with stage I to IV extragonadal tumors were enrolled. HDPEB treatment resulted in significantly improved 6-year EFS rate ± SE (89.6% ± 3.6% v 80.5% ± 4.8% for PEB; P = .0284). There was no significant difference in OS (HDPEB 91.7% ± 3.3% v PEB 86.0% ± 4.1%). Tumor-related deaths were more common after PEB (14 deaths v two deaths). Toxic deaths were more common with HDPEB (six deaths v one death). Other treatment-related toxicities were more common with HDPEB. Conclusion Combination chemotherapy with HDPEB significantly improves EFS for children with high-risk MGCT. The OS is similar in both regimens, and the significant toxicity associated with HDPEB limits its use.

Primary mediastinal germ cell tumors (M-GCTs) represent a heterogeneous group of tumors that varies with regard to age at presentation, histologic differentiation, and outcome. We retrospectively analyzed archival tissue samples of mediastinal mature and immature teratomas (n = 15) and malignant nonseminomatous M-GCTs (n = 20) with comparative genomic hybridization (CGH). The aim of this study was to define distinct genetic subgroups of M-GCT among the pediatric cohort that may differ in their clinical behavior and prognosis. All pure teratomas showed normal CGH profiles. Malignant M-GCTs in infants and children or = 8 years old. Additional recurrent changes included the loss of chromosome 13 and the gain of chromosome 21. All ten adolescents with malignant M-GCT were male, and five showed a gain of the X chromosome. In two of these five patients, Klinefelter syndrome was confirmed by cytogenetic analysis or by fluorescence in situ hybridization (FISH). In conclusion, CGH analysis of M-GCTs defines distinct genetic subgroups. Mediastinal teratomas show no genetic gains or losses. Malignant M-GCTs in children or = 8 years old show the same genetic profile previously reported in gonadal GCTs at this age. In addition, approximately 50% demonstrate a gain of the X chromosome, consistent with Klinefelter syndrome. Cooperative group studies reveal a significantly better prognosis of malignant M-GCT arising in infants compared to that in adolescents, suggesting that these genetic differences are associated with differences in clinical behavior.

Our new method provides a simple, robust, and accurate metric for determining the start of influenza season at the community level., Background. Early, accurate predictions of the onset of influenza season enable targeted implementation of control efforts. Our objective was to develop a tool to assist public health practitioners, researchers, and clinicians in defining the community-level onset of seasonal influenza epidemics. Methods. Using recent surveillance data on virologically confirmed infections of influenza, we developed the Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm, a method to identify the period of highest seasonal influenza activity. We used data from 2 large hospitals that serve Baltimore, Maryland and Denver, Colorado, and the surrounding geographic areas. The data used by ALERT are routinely collected surveillance data: weekly case counts of laboratory-confirmed influenza A virus. The main outcome is the percentage of prospective seasonal influenza cases identified by the ALERT algorithm. Results. When ALERT thresholds designed to capture 90% of all cases were applied prospectively to the 2011–2012 and 2012–2013 influenza seasons in both hospitals, 71%–91% of all reported cases fell within the ALERT period. Conclusions. The ALERT algorithm provides a simple, robust, and accurate metric for determining the onset of elevated influenza activity at the community level. This new algorithm provides valuable information that can impact infection prevention recommendations, public health practice, and healthcare delivery.

A prospective, randomized multicenter study was performed to evaluate the relative efficacy of two different concepts for early intensive therapy in a randomized trial of children with B-precursor acute lymphoblastic leukemia (ALL) at high risk (HR) for relapse. Four hundred and ninety eligible children with HR-ALL were randomized on the Pediatric Oncology Group (POG) 9006 phase III trial between 7 January 1991 and 12 January 1994. After prednisone (PDN), vincristine (VCR), asparaginase (ASP) and daunorubicin (DNR) induction, 470 patients received either 12 intensive parenteral treatments of intermediate dose (1 g/m2 each) methotrexate (MTX) and mercaptopurine (MP) over 24 weeks (regimen A) or 12 intensive course of alternating myelosuppressive drug combinations given over 30 weeks (regimen B). These drug combinations included MTX/MP, teniposide (VM-26)/cytosine arabinoside (AC) and VCR/PDN/DNR/AC/ASP. Central nervous system (CNS) prophylaxis was age-adjusted triple intrathecal chemotherapy. Patients with CNS disease at diagnosis were treated with craniospinal irradiation after the intensive phase. Continuation was standard doses of MTX and MP for 2 years. This trial was closed early because of an apparent early difference favoring regimen B. Results show that 470 patients achieved remission (97%). Two hundred and thirty two were randomized to regimen A and 238 to regimen B. The estimated 4-year event-free survival (EFS) for patients treated with regimen A is 61.6 % (s.e. = 3.3%) and with regimen B is 69.4% (s.e. = 3.1%), P = 0.091. Toxicities were more frequent on regimen B. In conclusion, for children with B-precursor ALL at high risk to relapse, early intensification with myelosuppressive combination chemotherapy was more toxic but produced no significant difference in EFS when compared to those treated with parenteral methotrexate and mercaptopurine.

Objective: In both adult women and children the potential for malignant recurrence from ovarian immature teratoma has prompted the standard use of chemotherapy after complete resection of the primary tumor. The efficacy of postoperative chemotherapy in children and adolescents with ovarian immature teratoma, however, has not been established. A pediatric intergroup trial (INT 0106) was designed to determine the need for postoperative chemotherapy in patients with ovarian immature teratoma after management with surgical resection only. Study Design: Between 1990 and 1995, 44 patients with completely resected ovarian immature tumor and without postoperative chemotherapy, who were able to undergo assessment, were accrued. Tumor tissue was evaluated by central pathology review to confirm diagnosis and determine tumor grading of immature neural elements. Patients were followed carefully for recurrence of disease with appropriate diagnostic imaging and serum marker studies. Results: Thirty-one patients had pure ovarian immature teratoma with a tumor grade of 1 (n = 17), 2 (n = 12), or 3 (n = 2). Age at diagnosis ranged between 1.5 and 15 years (median, 10). Of the 29 patients studied, the serum α-fetoprotein level was elevated in 10 (34%); the median level was 25 ng/ml. Thirteen patients had ovarian immature teratoma plus microscopic foci of yolk sac tumor. Tumor grade was 1, 2, or 3 in 1, 6, and 6 patients, respectively. Age ranged between 6 and 20 years (median, 12). In the 12 patients evaluated for serum α-fetoprotein, 10 (83%) had elevated levels; the median level was 262 ng/ml. The 4-year event-free and overall survival for the ovarian immature teratoma group and for the ovarian immature teratoma plus yolk sac tumor group was 97.7% (95% confidence interval, 84.9%-99.7%) and 100%, respectively. The only yolk sac tumor relapse occurred in a child with ovarian immature teratoma and yolk sac tumor who was then treated with chemotherapy and is alive and free of disease 57 months after recurrence. Conclusion: The results of this study suggest that surgery alone is curative for most children and adolescents with resected ovarian immature teratoma of any grade, even when elevated levels of serum α-fetoprotein or microscopic foci of yolk sac tumor are present. This experience strongly supports avoiding the long-term effects of chemotherapy in most children with ovarian immature teratoma by reserving postoperative therapy for cases with relapse. (Am J Obstet Gynecol 1999;181:353-8.)

Purpose: Infants with acute lymphoblastic leukemia (ALL) often enter remission; however, they have a high rate of relapse. To prevent relapse, infants' tolerance of and benefits from early intensive rotating drug pairs as part of therapy were studied. Methods: After prednisone, vincristine, asparaginase, and daunorubicin induction, 12 intensive treatments (ABACABA-CABAC) were administered in 30 weeks: A, intermediate dose methotrexate (MTX) and intermediate dose mercaptopurine (MP); B, cytosine arabinoside (Ara-C) and daunorubicin (DNR); C, Ara-C and teniposide (VM-26). Triple intrathecal chemotherapy (Ara-C, MTX, and hydrocortisone) was administered for central nervous system prophylaxis. Continuation therapy consisted of weekly MTX and daily MP for a total of 130 weeks of continuous complete remission. Results: Thirty-three infants (1 year old or younger) with newly diagnosed ALL were treated. Two infants did not respond to induction, 1 died from sepsis during continuation, I received a bone marrow transplant, and 24 relapsed. Median time to relapse was 39 weeks. The event-free survival rate at 5 years was 17% (standard error ± 7.7%). The most significant toxicities occurred during intensification and included fever-neutropenia and bacterial sepsis. Conclusion: Although early intensive rotating therapy is tolerable, the relapse-free survival rate remains poor for infants treated with the schedule on this protocol.

State University ofNew York at SyracuseObjective: Examine cognitive and academic late effects among children and adolescents who had receivedcentral nervous system (CNS) prophylactic chemotherapy alone for acute lymphocytic leukemia (ALL); nonehad received whole brain radiation therapy (RT).Method: Subjects included 47 children and adolescents from 5 to 22 years of age who were treated on thesame protocol and had been off treatment from 2 to 7 years at the time of assessment.Results: As a group the survivors displayed generally average performance on measures of cognitive and ac-ademic abilities, although they differed from normative means on tests of nonverbal skills. Girls performedmore poorly than the normative sample on nonverbal tasks, while no differences were found for boys. Ageat diagnosis and time off treatment were not significantly associated with cognitive and academic function-ing for survivors of this particular chemotherapy-only protocol.Conclusions: Data were interpreted to support generally modest potential late effects in specific areas forchildren and adolescents surviving ALL. These findings suggest a need for monitoring nonverbal cognitiveskills for childhood survivors of ALL, particularly for girls.

Levels of accumulation of methotrexate polyglutamates were measured in vitro in lymphoblasts obtained at diagnosis from children with B-progenitor cell acute lymphoblastic leukemia (pro-B ALL). They were compared to numerical and structural chromosomal abnormalities present in these leukemic cells. In a series of 95 patients, the percent with high lymphoblast methotrexate polyglutamate levels increased with the increase in modal number of total chromosomes (p0.001). Thus, lymphoblast methotrexate polyglutamate accumulation appeared to be closely linked to the extent of hyperdiploidy in childhood pro-B ALL. Lymphoblasts from 35 (88%) of the 40 children with hyperdiploid (50 chromosomes) and 23 (88%) of 26 with hyperdiploid (DNA Index1.16) pro-B ALL accumulated high levels of methotrexate polyglutamate, suggesting that they were more sensitive to methotrexate cytotoxicity. While children with hyperdiploid (DNA Index1.16) pro-B ALL have a good prognosis, those with trisomies of both chromosomes 4 and 10, almost all of whom are hyperdiploid, have an even better outcome. There was no significant difference in methotrexate polyglutamate levels in lymphoblasts from 19 children with and 21 without trisomies of both chromosomes 4 and 10 (p = 0.25). The improved response to multi-agent chemotherapy conferred by the presence of trisomies of both chromosomes 4 and 10 in such patients may be due to increased sensitivity of their lymphoblasts to one or more anti-leukemic agents in addition to methotrexate.

Plasma steady state methotrexate (MTX) level and red blood cell (RBC) MTX and folate concentrations were evaluated in 1124 children with newly diagnosed acute lymphoblastic leukemia enrolled in the Pediatric Oncology Group studies 9005 (lower risk; Regimens A and C) and 9006 (higher risk; Regimen A). These regimens included intermediate-dose MTX (1 g/m) given as a 24 hours infusion every other week for 12 doses during intensification. Plasma MTX level was evaluated at the end of MTX infusions. RBC MTX and folate concentrations were measured at the end of intensification. The 5 year continuous complete remission was 76±1.4% versus 85±3.0% for those patients with steady state MTX levels less than or equal to and greater than 14 μM, respectively (P=0.0125). Hispanic children had significantly reduced median steady state MTX levels, 8.7 μM, compared with non-Hispanic children, 9.95 μM (P=0.0015), but this did not correlate with a difference in outcome. Neither RBC MTX, RBC folate, nor the RBC MTX:folate ratio identified children at increased risk of failure.

Background. Children with chemotherapy refractory T-cell lymphoblastic leukemia/lymphoma were given alpha-interferon (α-IFN) to evaluate the efficacy and toxicity of this biologic response modifier. Methods. Twenty children with T-cell acute lymphoblastic leukemia (T-cell ALL) in marrow relapse and one patient with mediastinal recurrence of T-cell non-Hodgkin's lymphoma (T-cell NHL) were enrolled. All patients had failed at least two previous multiagent drug trials. Recombinant α-IFN was given at 30 million U/M2/dose intravenously or subcutaneously for 10 doses over 14 days, followed by 3 doses per week until disease progression occurred. Results.. One child had a complete response (

In the present study, the synthesis and secretion of transfected apolipoprotein (apo) A-IV was investigated in rat hepatoma McA-RH7777, a cell line that does not express apoA-IV mRNA or protein. An expression plasmid that contained the rat apoA-IV cDNA was transfected into the cells; five stable transformants were selected that harbor different copy numbers of the apoA-IV construct and secrete different amounts of apoA-IV. Gel filtration column chromatography and density gradient ultracentrifugation, combined with gel electrophoresis and electron microscopy techniques, demonstrated that (1) the secreted apoA-IV associated mainly with high-density lipoproteins (HDLs) and only a trace amount of apoA-IV was associated with very-low-density lipoproteins; (2) overexpression of apoA-IV resulted in an increased number of disk-shaped structures (thickness, approximately 8.0 nm and diameter, approximately 22 nm); and (3) the electrophoretic mobilities of the apoA-IV-containing particles differed from those of apoA-I-containing HDL. Expression of apoA-IV exerted no discernible effect on the density distribution or the secretion efficiency of apoB-100. Additionally, secretion of apoB-100 and apoA-IV exhibited opposite responses to serum: apoB-100 secretion was stimulated eightfold after addition of serum, whereas apoA-IV secretion was inhibited by 40%. These results suggest that synthesis of apoA-IV may lead to the formation of a subclass of HDL with a different metabolic fate than that of lipoproteins containing either apoA-I or apoB.

Background To prevent drug resistance, the authors designed a protocol that featured early intensive rotating drug pairs as part of the therapy for acute lymphoblastic leukemia (ALL). Methods After prednisone, vincristine, asparaginase, and daunorubicin induction, 12 intensive treatments (ABACABACABAC) were given in 30 weeks: A--intermediate-dose methotrexate (IDMTX) plus intermediate-dose mercaptopurine (MP); B--cytosine arabinoside (AC) plus daunorubicin (DNR); C--AC plus teniposide (VM-26). Triple intrathecal chemotherapy (AC, MTX, and hydrocortisone) was given for central nervous system (CNS) prophylaxis. Continuation therapy consisted of weekly MTX and daily MP until 2.5 years of continuous complete remission had been achieved. Results Seventy-four children (age range, 1-19 years) at high risk of relapse were treated. Of 55 with B-lineage (early pre-B, pre-B) ALL, 24 have failed (2 induction failures, 2 deaths from infection, and 20 relapses). The event-free survival (EFS) rate at 4 years was 55.5% (standard error [SE] +/- 7.7%). Of 19 patients with T-cell ALL, 12 have failed (2 induction failures and 10 relapses). The EFS rate at 4 years was 32.6% (SE +/- 26.8%). Toxicities were significantly more common after AC and DNR or AC and VM-26 than IDMTX and MP. There were no toxicity-related deaths during intensive treatments. Conclusion Early intensive rotating therapy is tolerable and warrants consideration for additional trials of patients with high-risk, B-lineage ALL.